ai data engineer

RKD - Nederlands Instituut voor Kunstgeschiedenis

<p><b>Het RKD &ndash; Nederlands Instituut voor Kunstgeschiedenis is het kennisinstituut voor de beeldende kunst van de Nederlanden. Wij beheren, ontsluiten en verbinden onze omvangrijke fysieke en digitale collectie voor kunsthistorisch onderzoek door professionals en studenten. De collectie bestaat uit rijke bronnen, zoals honderden archieven en een uitgebreide beeldbank met miljoenen afbeeldingen, over kunst van de late middeleeuwen tot heden.</b></p><p>De afdeling IT &amp; Innovatie is verantwoordelijk voor de digitale infrastructuur, informatievoorziening, projectleiding en technologische vernieuwing. Sinds mei 2026 hebben we de stap gezet van onderzoeks- naar kennisinstituut met een ambitieuze digitale transitie waarin IT en innovatie een steeds grotere rol spelen. De afdeling wordt de komende jaren verder versterkt en uitgebreid, met ruimte voor nieuwe expertise en initiatieven. Samenwerking staat centraal, zowel binnen de afdeling als daarbuiten. Je werkt in een omgeving waar IT, erfgoed en onderzoek samenkomen en waar jouw bijdrage direct impact heeft.</p><p>De afdeling IT &amp; Innovatie zoekt per direct een <b>AI &amp; Data Engineer</b> (36 uur per week).</p><p>Als AI &amp; Data Engineer ontwikkel je oplossingen die kunsthistorische data verrijken, verbinden en optimaliseren. Je werkt nauw samen met collectie- en informatiespecialisten en IT‑collega&rsquo;s en speelt een belangrijke rol in het datagedreven toekomstbeeld van het RKD.</p><p><b>Wat je gaat doen:</b></p><ul><li>Ontwikkelen en implementeren van AI/ML‑modellen voor metadata-verrijking;</li><li>Integreren van AI‑oplossingen in bestaande workflows en systemen;</li><li>Analyseren, opschonen, normaliseren en valideren van data. Inclusief het opsporen van fouten, inconsistenties en duplicaten;</li><li>Ontwikkelen en uitvoeren van automatische verrijkingsmethoden;</li><li>Ontwerpen en bouwen van data pipelines, Linked Data‑modellen (RDF, SPARQL) en datakoppelingen;</li><li>Borgen van schaalbaarheid, performance en stabiliteit binnen de data‑architectuur;</li><li>Ontwerpen van dataservices, API&rsquo;s en digitale functionaliteiten voor ontsluiting en analyse;</li><li>Vertalen van vragen naar datagedreven oplossingen en ontwikkelen van proof‑of‑concepts;</li><li>Adviseren van collega&rsquo;s over datagebruik en ondersteunen bij data‑analyses en visualisaties;</li><li>Actief bijdragen aan projecten in nauwe samenwerking.</li></ul><p><b>Wat wij vragen: </b></p><ul><li>Afgeronde wo-opleiding, bij voorkeur in AI, Data Science, Informatica, Digital Humanities of een vergelijkbaar vakgebied;</li><li>Ervaring met AI/ML‑ontwikkeling, data engineering of semantische dataverwerking;</li><li>Goede kennis van machine learning, dataverwerking, modelleren en datakwaliteit (opschonen, normaliseren, valideren);</li><li>Basis‑ tot gevorderde kennis van Linked Data, ontologie&euml;n, RDF en SPARQL;</li><li>Ervaring in het werken met domeinspecifieke datasets en trainingsdata;</li><li>Analytisch, nauwkeurig en procesgericht. Met oog voor reproduceerbaarheid en kwaliteitsstandaarden;</li><li>Innovatief en in staat om nieuwe idee&euml;n te vertalen naar praktische toepassingen;</li><li>Communicatief vaardig: je kunt technische concepten helder uitleggen aan collega&rsquo;s uit verschillende disciplines;</li><li>Samenwerkingsgericht en in staat multidisciplinaire oplossingen te ontwikkelen;</li><li>Bij voorkeur interesse in kunsthistorische data en digitale erfgoedtoepassingen;</li><li>Vermogen om AI‑ en datatechnieken verantwoord, uitlegbaar en AVG‑proof toe te passen;</li><li>Oog voor interoperabiliteit, datakwaliteit en duurzame datastructuren;</li><li>Bereidheid bij te dragen aan de doorontwikkeling van datagedreven werken binnen het RKD;</li><li>Flexibiliteit, initiatief en een proactieve houding.</li></ul><p><b>Wat wij bieden: </b></p><p>Een mooie functie in een zich ontwikkelende organisatie. De functie is gewaardeerd in schaal 10-11 (Museum cao), inschaling vindt plaats afhankelijk van kennis en ervaring. Het betreft in eerste instantie een arbeidsovereenkomst voor de duur van &eacute;&eacute;n jaar met zicht op een arbeidsovereenkomst voor onbepaalde tijd.</p><p>Daarnaast bieden wij:</p><ul><li>Werken in een vooraanstaand kennisinstituut met een creatieve werkomgeving;</li><li>De mogelijkheid tot persoonlijke ontwikkeling en veel afwisseling in je werk; ruimte voor initiatief en idee&euml;n;</li><li>Een breed scala aan verantwoordelijkheden en een flexibele en vriendelijke werkomgeving;</li><li>De kans je capaciteiten verder te ontwikkelen;</li><li>De mogelijkheid hybride te werken als je functie dat toelaat;</li><li>Uitstekende secundaire arbeidsvoorwaarden: een goede pensioenregeling, een volledige ov-reiskostenvergoeding vanaf 5 km (tot maximaal &euro; 399,95 per maand), thuiswerkfaciliteiten, een vakantietoeslag (8%) en een eindejaarsuitkering (3,4%);</li><li>Korting op je zorgverzekering via enkele aanbieders;</li><li>Een instellingslidmaatschap bij het ICOM waar medewerkers gebruik van kunnen maken. Met het ICOM-lidmaatschap krijg je onbeperkte toegang tot bijna alle musea ter wereld;</li><li>Een actieve personeelsvereniging, borrels en gezellige collega&rsquo;s.</li></ul><p><b>Solliciteren</b></p><p>Solliciteren kan t/m 5 juli 2026 door je motivatiebrief en cv te sturen naar vacatures@rkd.nl o.v.v. Vacature AI &amp; Data Engineer. Meer weten over de functie? Neem dan contact op met Astrid Verheusen, directeur-bestuurder,verheusen@rkd.nl. De eerste gesprekken staan gepland in de tweede helft van juli. Een assessment kan onderdeel uitmaken van de procedure. Het RKD vraagt om een geldige Verklaring Omtrent Gedrag (VOG) bij indiensttreding.</p><p>Het RKD is ervan overtuigd dat een verscheidenheid aan achtergronden en opvattingen ook ons instituut zeer ten goede komt. Daarom bouwen wij aan een personeelsbestand dat een afspiegeling is van onze samenleving, bestaande uit medewerkers met verschillende afkomst en vaardigheden die elkaar versterken. Vacatures staan nadrukkelijk open voor iedereen die zich in het profiel herkent.</p><p>Acquisitie naar aanleiding van deze advertentie wordt niet op prijs gesteld.</p><p>Salaris: &euro;3.768,00 - &euro;5.516,00 per maand</p><p>Arbeidsvoorwaarden:</p><ul><li>Collectieve zorgverzekering</li><li>Pensioen</li><li>Reiskostenvergoeding</li><li>Werk vanuit huis</li></ul><p>Opleiding:</p><ul><li>Master (Gewenst)</li></ul><p>Taal:</p><ul><li>Nederlands (Vereist)</li></ul><p>Toestemming om in Nederland te werken:</p><ul><li>Nederland (Vereist)</li></ul><p>Werklocatie: Fysiek</p>

AI & Data Engineer

MultiBank Group

<div>
 <p>
  Welcome to
  <strong>
   MultiBank Group
  </strong>
  , a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Role Overview:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  We are seeking an experienced
  <strong>
   AI Data Engineer
  </strong>
  to design, develop, and deploy innovative data and machine learning solutions that drive customer engagement, retention, and lifetime value (LTV). This role will focus on building scalable data pipelines, predictive models, and GenAI-powered applications that enable more personalised trading experiences and support data-driven decision-making across the business.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Collaborate with the wider marketing department to build meaningful datasets and a centralised data warehouse that will be used for reporting across numerous teams, focused on customer retention and LTV.
  </li>
  <li>
   Develop and own machine learning solutions that specialise in customer personalisation to increase engagement and coordinate with marketing teams towards their strategy and execution across multiple channels, especially around predictive modelling, churn prediction and customer segmentation.
  </li>
  <li>
   Explore the capabilities of Gen AI to develop new apps for our traders that make trading easier and more personalised based on their preferences.
  </li>
  <li>
   Maintain a commercial acumen and find opportunities where technology can be used to increase customer engagement or automate internal tasks.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Qualifications &amp; Requirements:
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Strong expertise in SQL, comfortable with raw data and multiple high-volume datasets such as customer journey, marketing performance or product behaviour.
  </li>
  <li>
   Comfortable creating pipelines and automations with Python to connect multiple endpoints and APIs such as first party databases, marketing platforms or other data collection platforms.
  </li>
  <li>
   Expertise in using Python todeploy machine learning solutions end to end, especially around predictive modelling and customer segmentation.
  </li>
  <li>
   Previous experience in finding customer insights through data analysis, communicating it to different stakeholders and strategizing on how to increase customer engagement and LTV.
  </li>
  <li>
   Capable of creating applications using LLMs that summarise high volumes of data and knowledge of the latest Gen AI use cases.
  </li>
  <li>
   Previous exposure to
   <strong>
    ETL tools
   </strong>
   (e.g., dbt, AWS Glue) and cloud environments (preferably
   <strong>
    AWS, SageMaker
   </strong>
   ).
  </li>
  <li>
   Strong communication skills with the ability to present complex data insights to non-technical stakeholders and support business strategy.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Why Join Us?
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <ul>
  <li>
   Work with an industry-leading global financial institution.
  </li>
  <li>
   Competitive salary and comprehensive employee benefits.
  </li>
  <li>
   Opportunities for professional growth and career advancement.
  </li>
  <li>
   Collaborative, inclusive, and dynamic work environment.
  </li>
  <li>
   Commitment to innovation and professional excellence.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <em>
   Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.
  </em>
 </p>
</div>


AI Data Engineer

Grace Hill

<div></div><div><div><b>Senior AI Data Engineer</b><br>
<i>AI-first data acquisition, serving &amp; reporting</i></div><h1 class="jobSectionHeader"><br>
<b>
About the role</b></h1><b>
This is an AI-first role</b> owning the data acquisition, serving, and reporting infrastructure that powers our product. We aggregate public data at scale, and these pipelines are core to what we deliver. We don&rsquo;t want someone to hand-operate scrapers and hand-debug every breakage&mdash;we want an engineer who builds systems that largely run, diagnose, and repair themselves, using LLMs and agentic workflows to keep everything reliable while continuously raising the level of automation.<br>
You own critical systems end to end with minimal hand-holding and treat AI as a core part of the toolkit. Remote, reporting to our Principal Engineer and team lead.<br>
<h1 class="jobSectionHeader"><b>
What you&rsquo;ll do</b></h1><ul><li><b>
Own and self-heal our fleet of web scrapers</b>&mdash;build LLM-assisted resilience so structural, markup, and anti-bot changes are detected, diagnosed, and self-repaired with minimal manual effort. When something does break, agents do the first pass on root-cause and propose fixes; you review and approve.</li><li>
Keep daily scraping runs stable&mdash;monitoring, alerting, retries, and graceful handling of upstream failures so data lands reliably each morning</li><li>
Use LLMs for resilient parsing and entity extraction from messy or changing HTML, reducing reliance on brittle selectors</li><li>
Own and optimize the serving layer and the ETL/ELT pipelines feeding our BigQuery warehouse&mdash;ensuring data is fresh, performant, and reliable for live use</li><li>
Build our reporting infrastructure&mdash;data models, transformations, and dashboards&mdash;plus AI-native layers like natural-language query and LLM-generated narrative insight</li><li>
Drive data quality through both rule-based checks and ML/LLM-based anomaly detection, and manage anti-bot challenges (proxies, rate limiting, request patterns) within legal and ethical guidelines</li><li>
Build and maintain production-grade MCP servers and agentic workflows that expose our data and tooling to internal and AI consumers</li><li>
Partner with the Principal Engineer, analysts, product, and leadership; document systems and best practices for maintainability and human-in-the-loop AI operations</li></ul><h1 class="jobSectionHeader"><b>
What we&rsquo;re looking for</b></h1><ul><li>
6+ years in data engineering, including ownership of mission-critical production systems</li><li>
Strong Python with deep experience building, maintaining, and debugging scrapers (e.g., Scrapy, Playwright, Selenium, BeautifulSoup)</li><li><b>
AI-first: </b>Hands-on experience building LLM-powered and agentic workflows in production&mdash;not just calling an API, but designing systems where agents do meaningful work under human supervision&mdash;including production-grade MCP servers</li><li>
Prompt engineering and LLM evaluation/observability&mdash;reasoning about output quality, cost, latency, and failure modes the way you&rsquo;d reason about uptime&mdash;plus fluency with AI-assisted dev tools (e.g., Claude Code, Cursor)</li><li>
Proven experience designing reporting/analytics layers&mdash;data modeling, transformations (e.g., dbt), and BI tools</li><li>
Hands-on with the GCP data stack&mdash;BigQuery, Cloud Composer (managed Airflow), Cloud Storage, Cloud Run or GKE&mdash;plus advanced SQL and Docker</li><li>
A reliability mindset&mdash;proven track record owning systems, triaging failures, and being accountable for uptime; sound judgment on when to use deterministic code versus an LLM</li><li>
Understanding of the legal and ethical considerations around web scraping</li></ul><h1 class="jobSectionHeader"><b>
Nice to have</b></h1><ul><li>
Experience training, deploying, and maintaining ML models</li><li>
Experience with MotherDuck / DuckDB, ideally serving data to production applications</li><li>
Experience scaling or refactoring distributed scraping systems</li><li>
Knowledge of Pub/Sub, Dataflow, or other large-scale data processing tools</li><li>
Infrastructure-as-code (Terraform)</li><li>
Experience setting data strategy or mentoring other engineers</li></ul><h1 class="jobSectionHeader"><b>
Logistics</b></h1><ul><li><b>
Location: </b>Remote (US based)</li><li><b>
On-call: </b>This role supports daily scraping and nightly processing runs and a production serving layer; some availability for off-hours incident response may be expected</li><li><b>
Compensation (based on experience): </b>$190-210K Base Salary + Bonus</li></ul><p>
Grace Hill offers a robust suite of benefits, including health, dental and vision insurance, 401K, PTO, life insurance, disability insurance, and more.<br>
<br>
Unfortunately we are not able to offer visa sponsorship or assistance. Applicants must be based in the US and authorized to work in the US at the time of hire.</p><h1 class="jobSectionHeader"><b><br>
About us</b></h1><p>
Grace Hill provides industry-leading SaaS technology solutions designed to make a positive impact in real estate and improve the lives of people where they work and live. Harnessing years of real estate experience and the understanding that people are better together, Grace Hill helps owners and operators increase property performance, reduce operating risk and grow top talent. More than 500,000 professionals from over 1,700 companies rely on Grace Hill&rsquo;s talent performance solutions covering policy, training, assessment, survey, and data-driven insights. Visit us at gracehill.com or on Linked<br>
<br>
Our <b>HelloData</b> product solves complex data problems for the multifamily industry, utilizing automated pipelines and AI to provide real-time market insights for the nation's top managers, developers, and investors. Our platform is trusted by the industry&rsquo;s largest operators to help optimize rents, underwrite operating expenses, and grow NOI with its highly accurate data and user-friendly interface. Since being acquired by Grace Hill in April 2025, HelloData has continued to accelerate at an unbelievable rate, growing ARR by over 300% in 2025 alone and on track for a record-breaking 2026. We combine the agility and innovation of a high-growth startup with the stability and resources of an established enterprise, making us the gold standard in multifamily data analytics.</p><p>
</p><p>9K5bQ2Xnlw</p></div>

Sr. AI Data Engineer

comply

<div><div><b>Who Are We:</b></div><div></div><div><b><br>
Comply</b> is the leading provider of compliance SaaS and consulting services for the global financial services sector. With more than 5,000 clients and hundreds of employees across the globe, Comply empowers Chief Compliance Officers and their teams to proactively manage regulatory obligations, mitigate risk, and scale with efficiency and confidence.</div><div></div><div><b><br>
Comply </b>serves thousands of global financial services clients including broker-dealers, insurers, investment banks, private funds, RIAs, and wealth managers who rely on Comply offerings to power their compliance programs.</div><div></div><div><br>
To learn more about Comply, visit comply.com</div><div></div><div><b><br>
The Role:</b></div>
<div></div>
<div>We are looking for Senior AI Data Engineers to implement and operationalize Comply&rsquo;s semantic layer &mdash; turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines. This is a hands-on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI-ready data products are reliable, performant, and adopted in practice. You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions.</div>
<h3 class="jobSectionHeader"><b>Responsibilities:
</b></h3><div>Semantic Layer Implementation</div>
<ul><li>Implement JSON-LD-based semantic models designed by the ontologist into production data systems</li>
<li>Build and maintain knowledge graph structures that reflect canonical domain models &bull; Develop and manage graph database schemas, queries, and data ingestion pipelines</li>
<li>Ensure semantic consistency between ontology definitions and downstream data product</li>
</ul><div>AI &amp; Vector Infrastructure</div>
<ul><li>Design and implement embedding pipelines that represent Comply&rsquo;s financial and regulatory data in<br>
vector space</li>
<li>Build and operate vector database infrastructure for semantic search and similarity retrieval</li>
<li>Implement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply&rsquo;s<br>
proprietary data</li>
<li>Evaluate and integrate LLM tooling and frameworks appropriate to Comply&rsquo;s use cases</li>
</ul><div>Data Pipeline &amp; Platform Engineering</div>
<ul><li>Build reliable, observable data pipelines that feed the semantic layer from upstream broker and<br>
regulatory data sources</li>
<li>Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs</li>
<li>Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts</li>
<li>Ensure the semantic layer scales with data volume and platform growth</li>
</ul><div>Collaboration &amp; Enablement</div>
<ul><li>Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent</li>
<li>Support consuming application teams in understanding and adopting AI-ready data products</li>
<li>Contribute to resolving cross-domain data integration challenges</li>
</ul><h3 class="jobSectionHeader"><b>Skills and Qualifications:
</b></h3><ul><li>Strong hands-on experience in data engineering, with a focus on semantic or AI data infrastructure</li>
<li>Experience building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon<br>
Neptune, or equivalent)</li>
<li>Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector)</li>
<li>Practical experience implementing RAG architectures or LLM-integrated data pipelines</li>
<li>Familiarity with semantic web standards &mdash; JSON-LD, RDF, OWL, or SKOS</li>
<li>Strong Python skills and experience with data pipeline frameworks</li>
<li>Experience with cloud-native data platforms (AWS, Azure, or GCP)</li>
<li>Exposure to domain-driven design (DDD) and bounded contexts is desirable.</li>
<li>Experience working directly with ontologists or knowledge engineers is a plus.</li>
<li>Familiarity with data contracts and data product frameworks is a plus.</li>
<li>Experience with DataOps tooling, data reliability, or data observability platforms is desirable.</li>
<li>Background in financial services, RegTech, or compliance data is a plus.</li>
</ul><div><b>To learn more about our values, mission and the wide-range of perks offered to employees at Comply, visit </b><b>https://www.comply.com/careers/</b><b>.<br>
</b></div><div></div><br>
<div>Comply is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.</div><div>
</div><div>
Applicants must be authorized to work for any employer in the United Kingdom. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time.</div><div>
</div><div>
Comply is aware of scammers posing as Comply employees and extending job offers via direct messaging, texts and social media platforms. These are fraudulent and should be treated as such. To learn more about this, please review our Statement of Fraudulent Job Offers.</div><div></div><p><br>
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.</p></div>

Senior AI Data Engineer

LSEG (London Stock Exchange Group)

<div><p><b>Role Overview:</b></p><p>
We are looking for an exceptional AI/GenAI Data Engineer to join our AI Platform team &mdash; one of the most technically ambitious initiatives at LSEG. This is not a role to maintain legacy pipelines. You will design and build the systems that make AI possible at HR-market scale.</p><p>
You will operate at the intersection of data engineering, machine learning infrastructure, and large language model applications. Your work will directly impact products used by the largest financial institutions in the world.</p><p></p><p><b>
Key responsibilities:</b></p><ul><li>
Design and operate high-throughput, low-latency data pipelines</li><li>
Build and maintain the feature stores, vector databases, and data layers that power production ML models and LLM-based applications across LSEG's product suite</li><li>
Architect SQL and NoSQL data systems to serve analytical and operational workloads with strict SLAs, and build real-time event streaming pipelines using Apache Kafka</li><li>
Implement Redis-based caching and pub/sub systems to support sub-millisecond data access for trading and analytics applications</li><li>
Build and deploy machine learning pipelines &mdash; from data preprocessing and feature engineering through model training, evaluation, and serving at scale</li><li>
Integrate and fine-tune large language models for different tasks</li><li>
Design and own CI/CD pipelines for data and ML systems, covering automated testing, model validation, and deployment automation</li><li>
Partner with Applied Research, Product Engineering, and Platform teams to translate AI research into reliable, production-grade systems</li></ul><p></p><p><b>
Must Have Skills:</b></p><ul><li>
LLM &amp; Generative AI: Hands-on experience building LLM-powered applications using APIs from OpenAI, Anthropic, or similar providers. Experience with RAG (Retrieval-Augmented Generation) pipelines, embedding models, vector stores, and frameworks such as LangChain or LlamaIndex. Familiarity with prompt engineering, LLM evaluation techniques, and fine-tuning is strongly preferred.</li><li>
MCP (Model Context Protocol): Understanding of MCP primitives for tool invocation, context injection, and secure LLM-to-system interaction.</li><li>
Machine Learning: Solid foundations in supervised and unsupervised machine learning using Scikit-learn, XGBoost, and PyTorch. You should have end-to-end ownership experience &mdash; from feature engineering and model training through evaluation, deployment, and production monitoring. Familiarity with MLflow or similar experiment tracking tools and an understanding of model drift detection are expected.</li><li>
Programming &mdash; Python &amp; Node.js: Strong Python is essential. You should be comfortable with async programming, writing production-grade APIs with FastAPI, data processing with Pandas and PySpark, and building clean, testable, well-documented code as a default. Node.js with TypeScript experience for event-driven backend services is a plus.</li><li>
SQL &mdash; ClickHouse, PostgreSQL &amp; Snowflake: Deep hands-on experience with at least two of ClickHouse, PostgreSQL, and Snowflake. You should be confident with complex query writing and optimisation, indexing strategies, partitioning, window functions, and understanding the trade-offs between OLTP and OLAP workloads. Experience with ClickHouse for high-performance analytical queries on time-series financial data is particularly valued.</li><li>
NoSQL &mdash; Elasticsearch &amp; Redis: Production experience with Elasticsearch for full-text search, aggregations, and log analytics &mdash; including index lifecycle management, mapping optimisation, and cluster configuration. Redis experience covering caching patterns, pub/sub messaging, Redis Streams, and session management in high-throughput environments.</li><li>
Streaming &mdash; Apache Kafka: Hands-on experience with Apache Kafka for building real-time event streaming pipelines. You should be comfortable with producer/consumer patterns, topic partitioning, consumer group management, and Kafka Connect for data integration. Experience with Kafka Streams or processing financial market events (order flow, price ticks, corporate actions) in a low-latency production environment is a strong advantage.</li><li>
CI/CD: Solid understanding of CI/CD principles with hands-on experience using GitHub Actions or Jenkins. You should be comfortable with Docker, Kubernetes, and writing Infrastructure as Code using Terraform or Helm. The expectation is that you treat deployment pipelines as production code &mdash; automated testing, rollback strategies, and environment parity are non-negotiable.</li><li>
Cloud &mdash; AWS: Primary cloud experience on AWS. You should have working knowledge of core data and compute services &mdash; S3, RDS, Redshift, Lambda, ECS/EKS, and SageMaker. Experience with cost optimisation and multi-region architecture design is a plus.</li></ul><p><b>
Qualifications:</b></p><ul><li>
7+ years of hands-on data engineering or ML engineering experience in production environments</li><li>
Strong system design instincts &mdash; you consider failure modes, backpressure, and graceful degradation as first-class concerns</li><li>
You default to observability &mdash; metrics, tracing, and alerts are built in before systems go live, not retrofitted after incidents</li><li>
You write the runbook. On-call ownership is something you embrace, not avoid</li><li>
Able to communicate technical trade-offs clearly to product managers, data scientists, and senior stakeholders</li><li>
Experience mentoring junior engineers and conducting meaningful technical code reviews</li><li>
Experience with HR data (time-series, tick data, reference data) is a significant advantage</li></ul><p><b>
Nice to have: (Review Certifications)</b></p><ul><li>
Cloud (AWS/Azure/GCP Data Engineer)</li><li>
Databricks or Spark-based certification</li><li>
ML / AI certification</li></ul><p></p><p><b>
We're proud to have been recognised as a Great Place to Work&reg; in India &lsquo;25.</b></p><p></p><p><b>
Career Stage:</b></p>
Senior Associate<p></p><p><b>
London Stock Exchange Group (LSEG) Information:</b></p><p>
Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.</p><p></p><p>
LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.</p><p></p><p>
Our purpose is the foundation on which our culture is built. Our values of <b>Integrity, Partnership</b>, <b>Excellence</b> and <b>Change</b> underpin our purpose and set the standard for everything we do, every day. They go to the heart of who we are and guide our decision making and everyday actions.</p><p></p><p>
Working with us means that you will be part of a dynamic organisation of 25,000 people across 65 countries. However, we will value your individuality and enable you to bring your true self to work so you can help enrich our diverse workforce.</p><p></p><p>
We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone&rsquo;s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.</p><p></p><p>
You will be part of a collaborative and creative culture where we encourage new ideas. We are committed to sustainability across our global business and we are proud to partner with our customers to help them meet their sustainability objectives. Our charity, the LSEG Foundation provides charitable grants to community groups that help people access economic opportunities and build a secure future with financial independence. Colleagues can get involved through fundraising and volunteering.</p><p></p><p>
LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.</p><p></p><p>
Please take a moment to read this privacy notice carefully, as it describes what personal information London Stock Exchange Group (LSEG) (we) may hold about you, what it&rsquo;s used for, and how it&rsquo;s obtained, your rights and how to contact us as a data subject.
</p><p></p><p>If you are submitting as a Recruitment Agency Partner, it is essential and your responsibility to ensure that candidates applying to LSEG are aware of this privacy notice.</p></div>

Deloitte

<div><div><div><div><h3 class="jobSectionHeader"><b>Basic information
</b></h3></div></div><div><div><div><div>Location
</div><div>London
</div></div><div><div>Business Line
</div><div>Tax &amp; Legal
</div></div><div><div>Job Type
</div><div>Contract
</div></div><div><div>Date published
</div><div>29-Jun-2026
</div></div><div><div>Req #
</div><div>23837
</div></div></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>Job description
</b></h3></div></div><div></div><div><div><div><div><p><b>Job Title:</b> AI Data Engineer (Azure)</p><p><b>
Location: </b>London Hybrid. Travel to the London office 2 days a week</p><p><b>
Duration: </b>6 months</p><p><b>
Contract Start Date:</b> ASAP</p><p><b>
Day rate:</b> Competitive (Inside IR35)</p><p></p><p><b><br>
Deloitte</b></p><p>
Working with the Deloitte Associate (Contractor) Programme means we can offer you the opportunity to work on a variation of industry and client related projects. Our aim is to retain the best talent and so when your project end date nears our team of Talent Community Advisors will be working with you to look at alternative projects within the firm that suit your experience should you wish to continue with Deloitte.</p><p></p><p><b><br>
The Role</b></p><p>
We are looking for a highly skilled <b>AI Data Engineer </b>(Contractor) specialising in <b>Azure </b>to join our Digital Innovation team, supporting cutting-edge data and analytics use cases across Tax &amp; Legal. This role is ideal for someone who combines <b>strong </b><b>AI/GenAI data pipeline</b><b> </b>expertise with <b>hands-on Azure, data discovery and analysis. </b>You will be comfortable working with unstructured data and modern Azure-native architectures. You will play a key role in transforming complex datasets into meaningful insights, working closely with Tax domain specialists to understand requirements, shape solutions, and clearly present outputs that drive business value. We're looking for a strong communicator with problem solving skills who can bridge technical data engineering and business understanding. A proactive, delivery-focused engineers who is comfortable working at pace.</p><p></p><p><b><br>
Deliverables: Responsibilities but not limited to:</b></p><b>
1. Data Engineering &amp; Azure Platform Delivery</b><br>
<ul><li><ul><li>
Design and build end-to-end data pipelines using Azure (ADF, Synapse, Fabric)</li><li>
Develop scalable frameworks for ingesting structured, semi-structured, and unstructured data</li><li>
Implement modern architectures (e.g. Lakehouse, medallion &ndash; bronze/silver/gold)</li><li>
Build and optimise storage solutions across ADLS Gen2, Cosmos DB, and Azure SQL/Synapse</li><li>
Deliver scalable, maintainable, production-ready solutions</li></ul></li></ul><b>
2. Unstructured Data &amp; NoSQL Engineering</b><ul><li><ul><li>
Design pipelines to process, transform, and enrich unstructured data.</li><li>
Leverage Cosmos DB for high-throughput, low-latency, schema-flexible workloads</li><li>
Optimise partitioning, indexing, and query performance</li><li>
Enable downstream analytics and AI use cases (e.g. reporting, search, RAG)</li></ul></li></ul><b>
3. Data Discovery, Analysis &amp; Business Engagement</b><ul><li><ul><li>
Conduct data discovery, profiling, and exploratory analysis</li><li>
Collaborate with Tax &amp; Legal stakeholders to translate business needs into data solutions</li><li>
Identify trends, anomalies, and data quality issues</li><li>
Present insights clearly to non-technical and senior stakeholders</li><li>
Support development of curated datasets and semantic layers</li></ul></li></ul>
4. <b>Collaboration &amp; Delivery</b><ul><li><ul><li>
Work with data scientists, AI engineers, architects, and domain specialists</li><li>
Enable data consumption via reporting tools, APIs, and data services</li><li>
Contribute to best practices in governance, lineage, and data cataloguing</li><li>
Deliver high-quality solutions in agile environments</li></ul></li></ul><p></p><p><b><br>
Essential Skills and Experience</b></p><ul><li><ul><li>
Proven experience as an <b>Azure Data Engineer </b>in end-to-end data platforms</li><li>
Exposure to <b>AI/GenAI data pipelines </b>(e.g. RAG, embeddings, search)</li><li>
Knowledge of <b>Azure AI Search</b> or vector-based retrieval systems</li><li>
Strong <b>hands-on</b> experience with:</li><li><ul><li><ul><li>
Azure Data Factory / Synapse / Microsoft Fabric</li><li>
Azure Data Lake Storage (ADLS Gen2)</li><li>
Azure Cosmos DB (Core/SQL API, NoSQL modelling)</li></ul></li></ul></li><li>
Experience working extensively with unstructured and semi-structured data</li><li>
Proficiency in <b>Python</b> and <b>SQL</b></li><li>
Demonstrated experience in:</li><li><ul><li><ul><li>
Data discovery, profiling, and exploratory analysis</li><li>
Data transformation and data quality frameworks</li></ul></li></ul></li><li>
Strong understanding of:</li><li><ul><li><ul><li>
NoSQL vs relational data modelling</li><li>
Data partitioning and performance tuning</li></ul></li></ul></li><li>
Experience building scalable, production-grade data pipelines</li><li>
Familiarity with CI/CD and DevOps practices in Azure</li></ul></li></ul><p></p><p><b><br>
Desirable:</b></p><ul><li><ul><li>
Azure Data Engineering Associate certification (DP-203) or equivalent</li><li>
Experience with Microsoft Fabric and Lakehouse architectures</li><li>
Experience integrating Cosmos DB with analytical platforms</li><li>
Familiarity with data governance frameworks</li></ul></li></ul><p></p><p><b><br>
IR35</b></p><p>
As a means of managing tax, commercial and reputational risks, Deloitte prohibits the use of Associates through Personal Service Companies (&lsquo;PSCs&rsquo;). All Associates must contract under PAYE arrangements through a Deloitte approved &lsquo;Employment Company&rsquo; (aka &lsquo;umbrella company.&rsquo;)</p></div></div></div></div></div></div><div></div>

Contract - AI Data Engineer (Azure)

EXL Service

<div><p><b>Job Description: Key Responsibilities
</b></p><ul><li>Design and develop <b>LLM-based applications </b>using single-agent or simple multi-agent patterns for business use cases
</li><li>Build and maintain <b>RAG pipelines </b>: data ingestion chunking embeddings retrieval response generation
</li><li>Implement <b>prompt engineering techniques </b>(prompt templates, chaining, basic tool/function calling)
</li><li>Develop backend services/APIs for AI applications using <b>Python frameworks (FastAPI / Flask / Streamlit)
</b></li><li>Integrate AI solutions with enterprise systems, databases, and APIs
</li><li>Apply basic <b>guardrails and validation checks </b>to improve response quality and reduce hallucination
</li><li>Work with Data Engineering teams to ensure <b>data quality, pipeline efficiency, and proper documentation
</b></li><li>Collaborate with MLOps teams for <b>deployment, monitoring, and iterative improvements
</b></li>Document solutions, reusable components, and best practices<br>
<li>
</li></ul><div></div><p><b>Must-Have Skills
</b></p><p><b>Experience
</b></p><ul><li><b>4&ndash;6 years total experience </b>, with <b>1+ year hands-on experience in GenAI / LLM-based applications<br>
</b></li></ul><div></div><p><b>LLM / GenAI &amp; Agentic Engineering
</b></p><ul><li>Strong hands-on experience with:
<ul><li>LLMs (Claude, OpenAI, etc.)
</li><li>RAG pipelines and retrieval optimisation
</li><li>GPT + Agentic AI implementation experience
</li></ul></li><li>Experience with:
<ul><li>LangChain, LangGraph, or similar frameworks
</li><li>Agent orchestration and tool-calling architectures
</li></ul></li>Deep understanding of:
LLM limitations, evaluation, and optimisation strategies<br>
<li><ul><li>
</li></ul></li></ul><div></div><p><b>Core Engineering
</b></p><ul><li>Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
</li><li>Deep data analysis experience and handling large volume of data
</li><li>Fabric/Azure Databricks/Snowflake data engineering integration skills
</li><li>Good exposure to:
<ul><li>Cloud platforms (Azure/AWS/GCP)
</li><li>SQL
</li>Containers, CI/CD, monitoring<br>
<li>
</li></ul></li></ul><div></div><p><b>Data / AI Foundations (Mandatory)
</b></p><p>Prior experience in one or more:
</p><ul><li>Data Engineering (ETL/ELT, pipelines, orchestration)
</li><li>Data Science / ML lifecycle (especially NLP)
</li>Analytics engineering / data products<br>
<li>
</li></ul><div></div><p><b>Good-to-Have / Preferred
</b></p><ul><li>Exposure to <b>model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
</b></li><li>Experience with <b>evaluation of LLM outputs (quality, relevance, latency)
</b></li><li>Understanding of <b>enterprise data privacy and security considerations in GenAI
</b></li><li>Exposure to <b>Azure AI / Azure OpenAI / AI Search ecosystems
</b></li><li>Experience working on <b>real client-facing AI solutions or POCs</b></li></ul></div><div></div>
<br><div><p><b>Responsibilities: Key Responsibilities
</b></p><ul><li>Design and develop <b>LLM-based applications </b>using single-agent or simple multi-agent patterns for business use cases
</li><li>Build and maintain <b>RAG pipelines </b>: data ingestion chunking embeddings retrieval response generation
</li><li>Implement <b>prompt engineering techniques </b>(prompt templates, chaining, basic tool/function calling)
</li><li>Develop backend services/APIs for AI applications using <b>Python frameworks (FastAPI / Flask / Streamlit)
</b></li><li>Integrate AI solutions with enterprise systems, databases, and APIs
</li><li>Apply basic <b>guardrails and validation checks </b>to improve response quality and reduce hallucination
</li><li>Work with Data Engineering teams to ensure <b>data quality, pipeline efficiency, and proper documentation
</b></li><li>Collaborate with MLOps teams for <b>deployment, monitoring, and iterative improvements
</b></li>Document solutions, reusable components, and best practices<br>
<li>
</li></ul><div></div><p><b>Must-Have Skills
</b></p><p><b>Experience
</b></p><ul><li><b>4&ndash;6 years total experience </b>, with <b>1+ year hands-on experience in GenAI / LLM-based applications<br>
</b></li></ul><div></div><p><b>LLM / GenAI &amp; Agentic Engineering
</b></p><ul><li>Strong hands-on experience with:
<ul><li>LLMs (Claude, OpenAI, etc.)
</li><li>RAG pipelines and retrieval optimisation
</li><li>GPT + Agentic AI implementation experience
</li></ul></li><li>Experience with:
<ul><li>LangChain, LangGraph, or similar frameworks
</li><li>Agent orchestration and tool-calling architectures
</li></ul></li>Deep understanding of:
LLM limitations, evaluation, and optimisation strategies<br>
<li><ul><li>
</li></ul></li></ul><div></div><p><b>Core Engineering
</b></p><ul><li>Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
</li><li>Deep data analysis experience and handling large volume of data
</li><li>Fabric/Azure Databricks/Snowflake data engineering integration skills
</li><li>Good exposure to:
<ul><li>Cloud platforms (Azure/AWS/GCP)
</li><li>SQL
</li>Containers, CI/CD, monitoring<br>
<li>
</li></ul></li></ul><div></div><p><b>Data / AI Foundations (Mandatory)
</b></p><p>Prior experience in one or more:
</p><ul><li>Data Engineering (ETL/ELT, pipelines, orchestration)
</li><li>Data Science / ML lifecycle (especially NLP)
</li>Analytics engineering / data products<br>
<li>
</li></ul><div></div><p><b>Good-to-Have / Preferred
</b></p><ul><li>Exposure to <b>model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
</b></li><li>Experience with <b>evaluation of LLM outputs (quality, relevance, latency)
</b></li><li>Understanding of <b>enterprise data privacy and security considerations in GenAI
</b></li><li>Exposure to <b>Azure AI / Azure OpenAI / AI Search ecosystems
</b></li><li>Experience working on <b>real client-facing AI solutions or POCs</b></li></ul></div><div></div>
<br><div><p><b>Qualifications: Key Responsibilities
</b></p><ul><li>Design and develop <b>LLM-based applications </b>using single-agent or simple multi-agent patterns for business use cases
</li><li>Build and maintain <b>RAG pipelines </b>: data ingestion chunking embeddings retrieval response generation
</li><li>Implement <b>prompt engineering techniques </b>(prompt templates, chaining, basic tool/function calling)
</li><li>Develop backend services/APIs for AI applications using <b>Python frameworks (FastAPI / Flask / Streamlit)
</b></li><li>Integrate AI solutions with enterprise systems, databases, and APIs
</li><li>Apply basic <b>guardrails and validation checks </b>to improve response quality and reduce hallucination
</li><li>Work with Data Engineering teams to ensure <b>data quality, pipeline efficiency, and proper documentation
</b></li><li>Collaborate with MLOps teams for <b>deployment, monitoring, and iterative improvements
</b></li>Document solutions, reusable components, and best practices<br>
<li>
</li></ul><div></div><p><b>Must-Have Skills
</b></p><p><b>Experience
</b></p><ul><li><b>4&ndash;6 years total experience </b>, with <b>1+ year hands-on experience in GenAI / LLM-based applications<br>
</b></li></ul><div></div><p><b>LLM / GenAI &amp; Agentic Engineering
</b></p><ul><li>Strong hands-on experience with:
<ul><li>LLMs (Claude, OpenAI, etc.)
</li><li>RAG pipelines and retrieval optimisation
</li><li>GPT + Agentic AI implementation experience
</li></ul></li><li>Experience with:
<ul><li>LangChain, LangGraph, or similar frameworks
</li><li>Agent orchestration and tool-calling architectures
</li></ul></li>Deep understanding of:
LLM limitations, evaluation, and optimisation strategies<br>
<li><ul><li>
</li></ul></li></ul><div></div><p><b>Core Engineering
</b></p><ul><li>Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
</li><li>Deep data analysis experience and handling large volume of data
</li><li>Fabric/Azure Databricks/Snowflake data engineering integration skills
</li><li>Good exposure to:
<ul><li>Cloud platforms (Azure/AWS/GCP)
</li><li>SQL
</li>Containers, CI/CD, monitoring<br>
<li>
</li></ul></li></ul><div></div><p><b>Data / AI Foundations (Mandatory)
</b></p><p>Prior experience in one or more:
</p><ul><li>Data Engineering (ETL/ELT, pipelines, orchestration)
</li><li>Data Science / ML lifecycle (especially NLP)
</li>Analytics engineering / data products<br>
<li>
</li></ul><div></div><p><b>Good-to-Have / Preferred
</b></p><ul><li>Exposure to <b>model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
</b></li><li>Experience with <b>evaluation of LLM outputs (quality, relevance, latency)
</b></li><li>Understanding of <b>enterprise data privacy and security considerations in GenAI
</b></li><li>Exposure to <b>Azure AI / Azure OpenAI / AI Search ecosystems
</b></li><li>Experience working on <b>real client-facing AI solutions or POCs</b></li></ul></div><div></div>

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