civil engineer

marketing executive

planning engineer

electrical engineer

electrician

ml engineer

MultiBank Group

<div>Welcome to MultiBank Group, 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.
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.
Role Overview
We are seeking a Senior Machine Learning Engineer to join our AI team as a technical owner of ML products and infrastructure. This is a deeply hands-on engineering position for someone who builds and scales production AI systems used by real users in real-time environments. The right candidate operates across the full ML lifecycle &mdash; from model design through deployment, optimization, and ongoing performance in production &mdash; and contributes to the technical direction of the AI platform.
Key Responsibilities<ul><li>
Architect and implement robust ML systems in production environments, ensuring scalability, reliability, and performance from day one</li><li>
Build and deploy supervised, unsupervised, deep learning, and generative AI models into live production environments at scale</li><li>
Own technical design for ML pipelines, feature stores, training infrastructure, and inference systems, driving decisions that balance performance, cost, and maintainability</li><li>
Design and deliver RAG systems, fine-tuning pipelines, prompt engineering frameworks, and evaluation pipelines for production-grade LLM applications</li><li>
Implement and maintain CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining pipelines</li><li>
Continuously optimize model performance, inference latency, cost efficiency, and reliability across live systems</li><li>
Collaborate with product managers, engineers, and data teams to translate business problems into scalable, maintainable AI solutions</li><li>
Mentor junior and mid-level ML engineers, establish best practices, and contribute to technical standards across the team</li><li>
Contribute to strategic decisions around data architecture, AI infrastructure, and cloud platform direction</li><li>
Work with mobile attribution and customer engagement data sources including Adjust, MoEngage, and Firebase for ML use cases such as churn prediction, personalization, and campaign optimization</li></ul>
Requirements<ul><li>
7 to 15 or more years of experience in software engineering, data science, or ML engineering</li><li>
Strong background in product companies, scale-ups, or enterprise AI platforms</li><li>
Proven track record of building production-grade AI systems, not solely notebooks or proof-of-concept work</li><li>
Comfortable owning systems end-to-end from data through model through deployment through monitoring</li><li>
Product-first engineering approach, not research-only profiles</li><li>
Advanced Python engineering skills with strong systems thinking and a focus on production quality</li><li>
Comfortable with fast iteration cycles and deploying models into live environments</li><li>
Ability to work directly and confidently with stakeholders and product owners</li><li>
Fintech or financial services experience is an advantage</li></ul>
Technical Skills
Machine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)
LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelines
MLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacks
Cloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP
Data Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures
Why Join Us?<ul><li>
Work with one of the world&rsquo;s leading financial derivatives institutions.</li><li>
Competitive salary plus performance-based incentives.</li><li>
Access to a dynamic, international, and fast-growing environment.</li><li>
Strong opportunities for career progression within a global financial group.</li><li>
Be part of a business committed to innovation, excellence, and long-term growth.</li></ul>
Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.
MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.</div>

Senior Machine Learning Engineer

Tether Operations Limited

<div>Join Tether and Shape the Future of Digital Finance
At Tether, we&rsquo;re not just building products, we&rsquo;re pioneering a global financial revolution. Our cutting-edge solutions empower businesses&mdash;from exchanges and wallets to payment processors and ATMs&mdash;to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.
Innovate with Tether
Tether Finance: Our innovative product suite features the world&rsquo;s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.
But that&rsquo;s just the beginning:
Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.
Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.
Tether Education: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.
Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.
Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world. If you&rsquo;re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We&rsquo;ve grown fast, stayed lean, and secured our place as a leader in the industry.
If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.
Are you ready to be part of the future?
About the job
We are developing a highly scalable media intelligence platform that processes, analyzes, and structures large volumes of multimedia content across text, image, video, and audio. As a Senior Applied ML Engineer, you will architect and build the core backend systems that power media ingestion, processing workflows, metadata generation, AI-based analysis, semantic search, and retrieval across large media libraries.
We are looking for a Senior Applied ML Engineer who can design, implement, optimize, and evaluate a production-grade moderation pipeline using open-source models.
This role requires deep backend engineering expertise, strong system design capability, and practical experience integrating AI/ML systems into production workflows. You will work on complex media-processing pipelines, video/audio analysis, OCR, speech-to-text, embedding generation, vector search, multimodal model integrations, and high-throughput asynchronous workloads. You will collaborate closely with engineering leadership to define backend architecture, improve reliability and scalability, and guide other engineers in delivering secure, observable, and high-performance systems.
Responsibilities
Backend Architecture &amp; System Ownership<ul><li>
Architect, build, and operate scalable backend services for a media intelligence platform, with a focus on clean, maintainable, and production-ready systems.</li><li>
Own critical backend components end to end, from system design and API contracts through implementation, deployment, monitoring, and iteration.</li><li>
Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model-serving workflows.</li><li>
Design data models and storage patterns for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit trails.</li><li>
Design high-throughput media ingestion and processing pipelines for large volumes of video, audio, image, and text content.</li><li>
Build distributed, event-driven workflows for media processing using queues and pub/sub systems such as SQS, Kafka, Pub/Sub, or equivalent technologies.</li><li>
Implement reliable asynchronous processing patterns, including retries, idempotency, dead-letter queues, backpressure handling, and fault-tolerant job execution.</li></ul>
AI/ML Integration &amp; Model Workflows<ul><li>
Lead the development and optimization of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows.</li><li>
Integrate and optimize AI/ML services within backend workflows, including model APIs, embedding pipelines, OCR, speech-to-text, scene analysis, multimodal inference, batching, caching, and fallback strategies.</li><li>
Collaborate with ML engineers, data scientists, or external model providers to benchmark models, compare quality/latency trade-offs, and safely roll out model upgrades.</li></ul>
Model Serving &amp; Performance Optimization<ul><li>
Optimize AI/ML inference workflows for latency, throughput, reliability, and cost across both real-time and batch-processing paths.</li><li>
Work with model-serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services to improve batching, concurrency, warmup behavior, timeout handling, autoscaling, and GPU utilization.</li><li>
Evaluate and apply practical model optimization techniques such as quantization, model distillation, batching, caching, prompt optimization, and routing to smaller or cheaper models where appropriate.</li><li>
Design and maintain vector search and indexing systems using technologies such as Pinecone, Weaviate, Qdrant, Elastic Vectors, FAISS, pgvector, or similar tools.</li><li>
Build retrieval workflows that support semantic search, similarity matching, duplicate detection, media discovery, and structured metadata search.</li><li>
Monitor model and system performance in production, including API latency, queue depth, processing time, model error rates, GPU utilization, confidence distributions, drift signals, and cost per processed item.Search, Indexing &amp; Data Retrieval</li></ul>
Infrastructure, Reliability &amp; Observability<ul><li>
Deploy and operate systems on AWS, GCP, Azure, or equivalent cloud platforms, including compute, storage, networking, queues, model-serving infrastructure, and monitoring systems.</li><li>
Ensure system reliability through logging, metrics, tracing, alerting, dashboards, operational runbooks, and incident-response best practices.</li></ul>
Collaboration &amp; Engineering Leadership<ul><li>
Collaborate with product, design, data, and ML teams to deliver media-rich, AI-powered product features.</li><li>
Mentor junior and mid-level engineers, support technical planning, review designs, and raise engineering quality across the team.</li><li>
Participate in code reviews, documentation, technical planning, and continuous improvement of engineering practices.</li><li>
Ensure code quality through testing, peer review, clear documentation, and maintainable implementation patterns.</li></ul> 
Education &amp; Experience<ul><li>
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.</li><li>
5-7+ years of backend engineering experience, ideally building scalable distributed systems, media platforms, data pipelines, or high-throughput backend services.</li><li>
Prior experience owning major backend modules end to end, including architecture, implementation, deployment, monitoring, and production operations.</li><li>
3 + years of experience integrating AI/ML inference systems into backend workflows, including model APIs, embedding pipelines, OCR, speech-to-text, scene detection, or multimodal model outputs.</li><li>
Hands-on experience creating AI-powered processing pipelines for image, video, audio, or text analysis.</li><li>
Practical experience with production model optimization, especially for image, video, embedding, or multimodal models, including batching, caching, quantization, prompt optimization, routing strategies, latency reduction, and cost optimization.</li><li>
Prior experience with vector search, semantic search, media retrieval, or similarity-matching systems is strongly preferred.</li><li>
Experience mentoring engineers, leading technical discussions, and influencing architectural decisions across backend, infrastructure, and AI/ML workflows.</li></ul>
Technical Skills<ul><li>
Strong expertise in Python and/or Node.js with Deep understanding of building scalable RESTful APIs and backend architectures</li><li>
Experience with HuggingFace transformers ecosystem and deep learning frameworks such as PyTorch and TensorFlow.</li><li>
Strong experience with SQL/NoSQL databases, schema design, and data modeling</li><li>
Preferred Exposure to distributed systems, microservices, asynchronous processing, and event-driven patterns with SQS, Pub/Sub, Kafka, or other queueing/pub-sub systems</li><li>
Experience deploying production systems on AWS, GCP, or similar cloud platforms</li><li>
Knowledge of infrastructure patterns (compute, storage, networking, observability)</li></ul>
AI/ML Integration<ul><li>
Experience orchestrating embedding generation, scene detection, OCR, speech-to-text, image classification, video analysis, and multimodal model integrations.</li><li>
Experience optimizing inference workflows for latency, throughput, reliability, and cost.</li><li>
Experience working with scalable and optimized inference settings, including tuning sampling parameters, managing output‑length formats, and configuring reasoning‑related behaviors.</li><li>
Familiarity with practical model optimization techniques such as batching, caching, quantization, model distillation, prompt optimization, fallback routing, and use of smaller models where appropriate.</li><li>
Experience working with model-serving systems such as vLLM, Triton, TGI, SageMaker, Vertex AI, or custom inference services is preferred.</li><li>
Experience working with LLM and Multi-modal evaluation and benchmarking frameworks and domain‑specific benchmarks with the ability to interpret results and optimize model performance accordingly.</li></ul>
System Design &amp; Architecture<ul><li>
Preferred understanding of distributed systems, scaling patterns, and performance engineering</li><li>
Ability to design modular, maintainable, and efficient architectures</li><li>
Experience with API versioning, modularization, and designing long-running workflows</li><li>
Understanding of performance bottlenecks and low-latency backend patterns</li></ul>
Important information for candidates 
Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles:<ul><li>
Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/</li><li>
Verify the recruiter&rsquo;s identity. All our recruiters have verified LinkedIn profiles. If you&rsquo;re unsure, you can confirm their identity by checking their profile or contacting us through our website.</li><li>
Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.</li><li>
Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io</li><li>
We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.</li></ul>
When in doubt, feel free to reach out through our official website.</div>

Machine Learning Systems Engineer

Jex Recruitment | Connecting top talent with leading companies

<div>
 
 We&rsquo;re looking for a hands-on Senior Data Scientist / ML Engineer to help build and deploy scalable machine learning solutions across financial products.
 
 
 This is a practical, business-focused role for someone who enjoys taking models from experimentation through to production and working closely with engineering, product, and business teams to drive measurable impact.
 
 
 
 
 
 What You&rsquo;ll Do:
 
 <ul>
 <li>
 Build and deploy machine learning models for real-world business problems
 </li>
 <li>
 Develop end-to-end ML pipelines including data preparation, training, validation, deployment, and monitoring
 </li>
 <li>
 Work with large and complex datasets to generate actionable insights
 </li>
 <li>
 Partner with engineering teams to productionize models and APIs
 </li>
 <li>
 Collaborate with stakeholders across product, risk, operations, and analytics
 </li>
 <li>
 Improve model performance, reliability, and scalability
 </li>
 <li>
 Contribute to MLOps, automation, and deployment best practices
 </li>
 <li>
 Support AI and advanced analytics initiatives across the business
 </li>
 </ul>
 
 
 
 
 Required Skills &amp; Experience:
 
 <ul>
 <li>
 6+ years of experience in Data Science, Machine Learning, or ML Engineering
 </li>
 <li>
 Strong Python and SQL skills
 </li>
 <li>
 Experience building and deploying ML models into production environments
 </li>
 <li>
 Strong understanding of machine learning fundamentals and model evaluation
 </li>
 <li>
 Experience working with cloud platforms (AWS, Azure, or GCP)
 </li>
 <li>
 Experience with tools/frameworks such as:
 </li>
 <li>
 scikit-learn
 </li>
 <li>
 PyTorch or TensorFlow
 </li>
 <li>
 Spark or Databricks
 </li>
 <li>
 Docker / CI-CD pipelines
 </li>
 <li>
 Experience building APIs or production services
 </li>
 <li>
 Ability to work with both technical and non-technical stakeholders
 </li>
 </ul>
 
 
 
 
 Preferred:
 
 <ul>
 <li>
 Banking, fintech, or financial services experience
 </li>
 <li>
 Experience with customer analytics, fraud, or decisioning models
 </li>
 <li>
 Familiarity with MLOps and model monitoring
 </li>
 <li>
 Exposure to NLP, LLMs, or Generative AI use cases
 </li>
 <li>
 Experience with Airflow, MLflow, Kubernetes, or Hadoop ecosystems
 </li>
 </ul>
 
 
 
 
 We value people who:
 
 <ul>
 <li>
 Take ownership and solve problems end-to-end
 </li>
 <li>
 Can balance experimentation with production delivery
 </li>
 <li>
 Think commercially and focus on business outcomes
 </li>
 <li>
 Communicate clearly and work well across teams
 </li>
 <li>
 Enjoy building scalable, reliable systems
 </li>
 </ul>
 
 Tech Environment
 
 
 Python &bull; SQL &bull; Databricks &bull; Spark &bull; Docker &bull; APIs &bull; Cloud Platforms &bull; MLflow &bull; CI/CD &bull; Power BI
 
 
 
</div>

Data Scientist

Marcura

<div>The Managed Port Calls team requires a Staff AI Engineer to drive AI innovation across our Marcura platform. This role exists to architect and deliver AI-powered features that enhance operational efficiency, predictive capabilities, and decision-making for stakeholders.
The role is hands-on execution&mdash;building production-grade AI systems, mentoring engineers on AI/ML best practices, and selecting appropriate technologies and frameworks. Working closely with product, backend, and data teams, you'll embed intelligence throughout the platform while ensuring our AI solutions are scalable, reliable, and deliver measurable business value.
Sample use cases that the role will require to build and manage:
<ul><li>Convert manual processes to fully automated processes</li><li>
Partially automate processes when full automation is not achievable</li><li>
Help improve efficiency for both operations team and customers</li><li>
Reduce the amount of manual (especially repetitive) work mainly for operations team</li><li>
Predict answers to questions based on an existing historical knowledge base</li><li>
Predict costs based on historical knowledge</li><li>
Chat bot style task automation</li></ul> 

Requirements 
<ul><li>
Bachelor's Degree in Information Technology, Data Science, Machine Learning/AI, Computer Science or other relevant fields</li><li>
Minimum of 8 years of experience in relevant fields including data science and AI
</li></ul>Machine Learning &amp; AI:<ul><li>
Experience with LLMs and building AI agents using frameworks like LangChain, LangGraph, and LangSmith</li><li>
Hands-on experience with prompt engineering, RAG (Retrieval-Augmented Generation), and LLM orchestration</li><li>
Knowledge of AI agent design patterns: reasoning, planning, tool use, and memory management</li><li>
Proven experience designing, training, and deploying ML models in production environments</li><li>
Understanding of ML fundamentals and MLOps practices: model versioning, monitoring, CI/CD pipelines</li><li>
Proficiency in machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch).</li></ul>
Programming:<ul><li>
Strong coding skills in languages such as Python,
</li><li>Good to have programming experience in Java,</li><li>
Familiarity with data manipulation and analysis libraries (e.g., Pandas, NumPy).</li></ul>
Cloud Computing:<ul><li>
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for deploying AI models. [Azure is the preference]</li></ul>
Software Development:<ul><li>
Understanding of software development life cycle (SDLC) and agile methodologies.</li><li>
Experience with version control systems (e.g., Git).</li></ul>
Data Analysis:<ul><li>
Ability to analyze and interpret complex datasets.</li></ul>
Problem-Solving:<ul><li>
Strong analytical thinking to identify and solve operational inefficiencies.</li><li>
Ability to design and implement AI-driven solutions.</li></ul>
Communication:<ul><li>
Ability to explain complex technical concepts to non-technical stakeholders.</li><li>
Strong written and verbal communication skills.</li></ul>
Collaboration:<ul><li>
Experience working in cross-functional teams.</li><li>
Ability to collaborate with other developers, data scientists, and business analysts.</li></ul> 

Benefits 
<ul><li>
Competitive Salary and Bonus: We reward your expertise and contributions.</li><li>
Inclusive Onboarding Experience: Our onboarding program is designed to set you up for success right from day one.</li><li>
Marcura Wellness Zone: We value your work-life balance and well-being.</li><li>
Global Opportunities: Be part of an ambitious, expanding company with a local touch.</li><li>
Diverse, Supportive Work Culture: We&rsquo;re committed to inclusion, diversity, and a sense of belonging for all team members.</li></ul></div>

Generative AI Solutions Engineer

Dyson

<div>
 
 About Us
 
 
 
 At Dyson, we&rsquo;re driven by a relentless pursuit of innovation&mdash;pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson&rsquo;s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
 
 
 You&rsquo;ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
 
 
 
 About The Role
 
 
 
 We are looking for a specialized Lead Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
 </li>
 <li>
 Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
 </li>
 <li>
 Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
 </li>
 <li>
 Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
 </li>
 <li>
 Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
 </li>
 <li>
 Set up and maintain robust data preparation infrastructure&mdash;optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.
 </li>
 <li>
 Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You&rsquo;ll help transform raw data into actionable insight, supporting both research and deployment.
 </li>
 <li>
 Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.
 
 
 
 </li>
 </ul>
 
 About you
 
 
 
 <ul>
 <li>
 At least 8+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.
 </li>
 <li>
 Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
 </li>
 <li>
 Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).
 </li>
 <li>
 Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).
 </li>
 <li>
 Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.
 </li>
 <li>
 Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.
 </li>
 <li>
 Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.
 </li>
 <li>
 Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).
 </li>
 <li>
 Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).
 </li>
 <li>
 Strong background in feature engineering, data analysis, and data visualization&mdash;comfortable using tools like Jupyter, Tableau, or Power BI.
 </li>
 <li>
 Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
 </li>
 <li>
 Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.
 </li>
 <li>
 Bachelor&rsquo;s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
 
 
 
 </li>
 </ul>
 Dyson is an equal opportunity employer. We know that great minds don&rsquo;t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
</div>

Lead Data Intelligence Machine Learning Engineer

sandan AI

<div>Standort: Remote (DACH-Zeitzone), Office Berlin optional 
Anstellung: Vollzeit, unbefristet 
Start: Ab sofort 
Sprache: Deutsch C2, Englisch min. C1


&Uuml;BER SANDAN AI

sandan AI ist ein in Berlin ansässiges AI-Native-Startup für Enterprise-Kunden im DACH-Raum. Wir prägen, wie Marketing in einer agentischen Wirtschaft aussieht &ndash; und bauen die Plattform, auf der diese neue Generation von Marketing-Workflows läuft. Unser Anspruch: Software so zu entwickeln, wie Software 2026 entwickelt werden sollte &ndash; agentisch, iterativ, mit AI als integralem Bestandteil des EngineeringWorkflows.


UNSERE ARBEITSWEISE 
Wir entwickeln agentisch. Claude Code und Codex sind Kern unseres Engineering-Workflows &ndash; nicht Spielerei. Wer bei uns einsteigt, arbeitet entweder schon produktiv mit diesen Tools oder will sich aktiv und tief einarbeiten.
<h2 class="jobSectionHeader">
Aufgaben</h2>

Du baust die Intelligenz, die in ArcGEN l&auml;uft. Du verantwortest unsere LLM-Pipelines, Agenten-Architekturen und Eval-Frameworks - vom Prototypen im Notebook bis zur produktiven Pipeline mit nachweisbarer Qualit&auml;t.
<ul>
<li>Design und Betrieb von RAG-Pipelines (Embeddings, Vector Stores, Re-Ranking)</li>
<li>Architektur agentischer Systeme &ndash; Tool-Calling, MCP, Sub-Agent-Orchestrierung</li>
<li>Aufbau und Pflege von LLM-Eval-Frameworks: Metriken, Benchmarks, A/B-Testing, Quality-Gates</li>
<li>Prompt-Engineering und systematische Prompt-Optimierung</li>
<li>Entwicklung ML-Modelle f&uuml;r Marketing-Use-Cases (Attribution, Targeting, Forecasting, Anomaly Detection)</li>
<li>&Uuml;berf&uuml;hrung von Notebook-Explorationen in produktionsreife Pipelines</li>
<li>Enge Zusammenarbeit mit unseren Full-Stack-Engineers an der Plattform-Integration</li>
</ul><h2 class="jobSectionHeader">Qualifikation</h2>

Must-have:
<ul>
<li>Praxis mit modernen LLM-Workflows in Production: RAG, Embeddings, Vector Databases (Pinecone, Weaviate, pgvector)</li>
<li>Solide Python-Kenntnisse inkl. pandas, NumPy, scikit-learn sowie PyTorch oder TensorFlow</li>
<li>Erfahrung im End-to-End-ML-Lifecycle: Datenaufbereitung, Training, Evaluation, Deployment, Monitoring</li>
<li>Erfahrung mit systematischer Modell-Evaluation: Metriken, Benchmarks, Eval-Frameworks</li>
<li>Deutsch C2 und Englisch C1</li>
<li>Builder-Mentalit&auml;t: Du lieferst Production-Systeme, nicht nur Notebooks</li>
</ul>
Pluspunkte:
<ul>
<li>Eigenentwicklung von Custom Agents, Sub-Agents oder spezialisierten Coding-Workflows</li>
<li>Erfahrung mit MCP (Model Context Protocol) und Tool-Calling-Architekturen</li>
<li>Praxis mit Agent-Frameworks (Mastra, LangGraph, Vercel AI SDK)</li>
<li>Erfahrung mit Fine-Tuning, LoRA-Adaptern oder Distillation</li>
<li>MLOps-Tools (MLflow, Weights &amp; Biases, DVC)</li>
<li>Background in Marketing-Analytics, Attribution-Modellen oder Performance-Marketing-Daten</li>
<li>Kaggle, Open-Source-Beitr&auml;ge oder Publikationen im ML-/AI-Bereich</li>
</ul><h2 class="jobSectionHeader">Benefits</h2>
<ul><li>Wettbewerbsf&auml;higes Gehalt mit j&auml;hrlicher Anpassung</li>
<li>Vollst&auml;ndig finanzierter Zugang zu Claude Max, Codex, GitHub Copilot, OpenAI- und Anthropic-APIs sowie allen relevanten LLM-Tools</li>
<li>Compute-Budget f&uuml;r Experimente, Modell-Training und ML-Workloads</li>
<li>MacBook Pro nach Wahl</li>
<li>Remote-First, flexible Arbeitszeiten</li>
<li>Quartalsweise Team-Meetups in Berlin oder Dubai (DIFC AI Campus, optional)</li>
<li>Direkter Einfluss auf Produkt-Architektur in einer fr&uuml;hen Phase</li>
<li>Steile Lernkurve in Agentic AI, LLM-Orchestrierung und AI-Native-Engineering</li>
</ul>
Lebenslauf reicht. Wenn du GitHub, Portfolio, Kaggle oder ein 
Open-Source-Projekt hast &ndash; sehr gerne mit dazu.

Wir freuen uns auf deine Bewerbung!</div>

AI Engineer (m/w/d) - LLM Apps, RAG & Argentic Systems (german speaking)

Seattle Aviation Solutions

<div>
 
 About Seattle Aviation Solutions (SAS)
 
 
 
 
 
 Seattle Aviation Solutions (SAS) is a leading global distributor of aftermarket parts for business and commercial aircraft. Headquartered in Seattle, with operational hubs in Dubai and Turkey, SAS is committed to providing exceptional service and quality products to its clients worldwide. We are leveraging data and AI to revolutionize the way aviation parts are sourced, sold, and managed.
 
 
 
 
 
 Position: AI Associate
 
 
 
 
 
 Are you a technically brilliant and self-directed developer with a passion for working with large datasets and exploring the frontier of AI? Do you enjoy building elegant solutions with Python and want to play a foundational role in shaping a company's data and AI strategy? If so, we invite you to join Seattle Aviation Solutions as an AI Associate.
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 
 - Data Engineering &amp; Pipeline Development: Build and maintain scalable, reliable data pipelines and ETL processes to support analytics, reporting, and AI applications.
 
 
 
 
 
 - AI/ML Enablement: Collaborate with internal stakeholders to identify AI opportunities and develop prototypes that support business functions such as pricing, purchasing optimization, forecasting, and anomaly detection.
 
 
 
 
 
 - Python Development: Write clean, efficient, modular Python code to support data transformation, automation, web scraping, API integrations, and ML model deployment.
 
 
 
 
 
 - Data Architecture: Help define and maintain data infrastructure, schemas, and storage systems to ensure high-performance analytics across structured and unstructured datasets.
 
 
 
 
 
 - Independent Problem Solving: Take ownership of technical challenges, working independently to design and implement end-to-end solutions with minimal oversight.
 
 
 
 
 
 - Tooling and Infrastructure: Set up and manage tools for data analysis, model development, and experimentation (e.g., Jupyter, Airflow, Docker, Git).
 
 
 
 
 
 - Collaboration: Work closely with data and IT leadership and other technical team members to translate high-level ideas into deployable code and functional systems.
 
 
 
 
 
 - R&amp;D: Stay current on AI/ML trends, LLMs, and emerging technologies to help SAS map its AI future and continuously push innovation forward.
 
 
 
 
 
 Personal Attributes
 
 
 
 
 
 - Highly intelligent, curious, and motivated by technical challenges.
 
 
 - Strong problem-solver with a hacker mindset and bias for action.
 
 
 - Self-directed and capable of managing projects independently from concept to deployment.
 
 
 - Excellent communication skills and ability to work across technical and non-technical teams.
 
 
 
 
 
 Qualifications
 
 
 
 
 
 Experience: 2 to 4 years of experience in Python development, data engineering, or AI/ML roles.
 
 
 
 
 
 Skills:
 
 
 - Strong Python programming expertise.
 
 
 - Experience with data processing frameworks (e.g., Pandas, NumPy, SQLAlchemy, PySpark).
 
 
 - Familiarity with cloud environments (e.g., AWS, GCP, or Azure).
 
 
 - Proficiency with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Redis).
 
 
 - Experience building and deploying ML models using libraries like scikit-learn, TensorFlow, or PyTorch.
 
 
 - Comfortable with DevOps tools (Git, Docker, CI/CD pipelines) and working in Linux environments.
 
 
 - Understanding of REST APIs and third-party data integrations.
 
 
 - Experience working with large datasets and solving performance bottlenecks.
 
 
 - Comfortable working within the framework of current tech stack (Snowflake, AWS, Fivetran, Tableau).
 
 
 
 
 
 Bonus: Exposure to LLMs, LangChain, Vector Databases (e.g., Pinecone, Weaviate), or NLP applications is highly desirable.
 
 
 
 
 
 Location: Dubai, Night Shift (willingness to work either EST or PST)
 
 
 
 
 
 Job Type: Full-time
 
 
 
 
 
 Benefits
 
 
 
 
 
 - Competitive salary and performance-based incentives
 
 
 - Comprehensive health insurance
 
 
 - Opportunity to define and build the AI roadmap of a global aviation company
 
</div>

Artificial Intelligence Specialist

Explore 494+ Ml engineer in Dubai this June. Find roles across top companies and apply now to boost your career.