ai engineer

analytics engineer

infrastructure engineer

engineering director

engineering manager

engineering agent

Understanding Recruitment

<div>
 
 
 AI Engineer &ndash; Agentic AI
 
 
 
 
 
 Want to build agentic AI that goes beyond prototypes and actually operates in high-stakes environments?
 
 
 
 
 
 
 
 This is a chance to build
 
 production AI systems from the ground up
 
 , with real ownership over architecture, model selection and deployment.
 
 
 
 
 
 What&rsquo;s in it for you?
 
 <ul>
 <li>
 Build
 
 agentic AI systems
 
 that interact with tools and take actions
 </li>
 <li>
 Work on challenging problems around
 
 reliability, security and controlled AI deployment
 
 </li>
 <li>
 Potential to work across
 
 multimodal AI, perception and real-time sensor data
 
 </li>
 <li>
 High levels of technical ownership from day one
 </li>
 <li>
 Competitive salary +
 
 equity
 
 </li>
 <li>
 
 7% pension
 
 , private healthcare and free daily meals
 </li>
 </ul>
 
 
 
 
 What you&rsquo;ll be doing
 
 <ul>
 <li>
 Building core
 
 agentic AI product features
 
 </li>
 <li>
 Designing LLM architectures and agent workflows
 </li>
 <li>
 Evaluating models and choosing the right approach for each problem
 </li>
 <li>
 Integrating AI into complex production software
 </li>
 <li>
 Deploying across
 
 cloud and on-premise environments
 
 </li>
 <li>
 Improving the security, reliability and testing of AI systems
 </li>
 <li>
 Potentially working with
 
 object detection and multimodal models
 
 </li>
 </ul>
 
 
 
 
 What you&rsquo;ll bring
 
 <ul>
 <li>
 Experience building
 
 ML or AI systems in production
 
 </li>
 <li>
 Hands-on experience with
 
 LLMs and agentic AI
 
 </li>
 <li>
 Strong
 
 Python, Rust, C++ or Java
 
 </li>
 <li>
 Experience with
 
 PyTorch, JAX or TensorFlow
 
 </li>
 <li>
 Exposure to
 
 LangGraph, Google ADK or similar
 
 </li>
 <li>
 Strong understanding of ML fundamentals and model trade-offs
 </li>
 </ul>
 
 
 
 
 Experience with
 
 MLOps, multimodal AI or constrained hardware
 
 is a bonus rather than a requirement.
 
 
 
 
 
 You&rsquo;ll need the
 
 right to work in the UK
 
 and be eligible for
 
 UK SC clearance
 
 . This is an
 
 office-first role
 
 with some flexibility.
 
</div>

AI Engineer - Agentic AI

Intellias

<div>
 
 Our client is a leading global investment management company headquartered in London. It manages over $228 billion in assets and serves institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide. The firm specializes in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management. Data science, machine learning, and AI are core components of its investment and research processes.
 
 
 As part of our collaboration we will focus on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise: Agentic Security and AI-Ready Data Foundations.
 
 
 
 Project overview:
 
 
 <ul>
 <li>
 We build the data foundations and evaluation frameworks that make AI useful, reliable and safe inside regulated financial firms. The value of an AI agent depends not only on the models behind it, but also on the quality of the structured and unstructured data it consumes and the accuracy, relevance and traceability of the outputs it produces. Your job is to measure that quality, identify where it breaks down and turn the findings into practical improvements.
 </li>
 <li>
 This is an
 
 engineering
 
 role, not an analytical one. You will build the agentic workflows that reason over the firm's research content, and the ingestion, evaluation and guardrail tooling that makes their output trustworthy enough for investment professionals to act on. None of this tooling exists today &mdash; you would be building it from scratch.
 </li>
 </ul>
 
 
 Requirements:
 
 
 <ul>
 <li>
 Proven experience
 
 building production agentic and LLM systems
 
 &mdash; multi-agent or orchestrated workflows that reason across heterogeneous sources (PDFs, audio transcripts, file shares, databases) and surface confidence, gaps and provenance back to end users.
 </li>
 <li>
 Hands-on experience
 
 engineering document ingestion and extraction pipelines
 
 : parsing, chunking and the automated quality controls around them &mdash; detecting empty or truncated content, vendor feeds delivering the wrong section of a document, duplication, encoding and OCR defects.
 </li>
 <li>
 Experience
 
 building evaluation and guardrail infrastructure
 
 for AI systems: groundedness scoring, citation and provenance (file name plus the exact snippet retrieved), eval harnesses, regression suites and LLM observability.
 </li>
 <li>
 
 Strong production Python engineering
 
 &mdash; services and pipelines that run unattended, with testing, CI and code standards. This is not a notebook-and-analysis role.
 </li>
 <li>
 Able to work from a deliberately vague brief, shape the problem directly with business stakeholders, and explain technical results to non-technical audiences.
 </li>
 </ul>
 
 
 Nice to have
 
 
 <ul>
 <li>
 Experience tuning retrieval quality &mdash; chunking strategy, embedding choice, retrieval evaluation.
 </li>
 <li>
 Structured and time-series data-quality experience (coverage gaps, nulls in critical columns).
 </li>
 <li>
 ETL pipelines and fluency in SQL.
 </li>
 <li>
 Previous experience working with investment professionals in a fast-paced environment.
 </li>
 <li>
 Working knowledge of Snowflake, Linux/UNIX, Git, Jira.
 </li>
 </ul>
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 
 Build agentic workflows
 
 that reason over research reports, transcripts, filings and news, and present portfolio managers with a clear view of what was found, what is missing, and how confident the system is in each answer.
 </li>
 <li>
 
 Engineer automated quality checks on unstructured source content
 
 before ingestion &mdash; empty or blank content, truncation, extraction fidelity, coverage gaps across expected document sets.
 </li>
 <li>
 
 Build vendor delivery validation
 
 : detect and quantify parsing and format defects in incoming feeds, feed them back to vendors and the data sourcing team, and fix extraction where it sits with us.
 </li>
 <li>
 
 Build evaluation harnesses, benchmarks and guardrails
 
 for agent output &mdash; groundedness, factual accuracy, relevance, and citation/provenance, so any claim can be traced back to a specific file and snippet.
 </li>
 <li>
 
 Ship monitoring and dashboards
 
 surfacing data-quality findings, confidence levels and coverage gaps to both engineering and PM audiences.
 </li>
 <li>
 Work directly with the platform engineering team, data sourcing, and portfolio managers to turn business expectations into measurable, automated quality standards.
 </li>
 </ul>
 
 
 Why this position:
 
 
 <ul>
 <li>
 This role sits at the intersection of data engineering, AI, and financial services, solving one of the most important challenges in enterprise AI: enabling agents to securely access and reason over trusted data. You'll have the opportunity to design and build foundational platforms that combine large-scale data systems, governance, and AI technologies in highly regulated environments. It offers significant technical ownership, exposure to cutting-edge AI agent architectures, and the chance to shape how organisations safely unlock value from their data.
 </li>
 </ul>
 
 
 
</div>

Senior AI Engineer

International SOS

<div>
 
 About The Role
 
 
 
 We are looking for an experienced Senior Machine Learning Engineer to join our Product team, in Chiswick, West London,
 
 
 You will be responsible for building production-grade AI capabilities across the platform&rsquo;s generative and agentic paradigms &ndash; from retrieval-augmented knowledge assistants and context-aware responses to multi-step agent workflows. The role combines strong machine learning and software engineering skills to deliver grounded, governed, and scalable solutions that move from Lab prototype to Factory production.
 
 
 This is an excellent opportunity for an experienced engineer, looking for their next move in a global organization.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and implement ML, generative, and agentic AI solutions &mdash; RAG pipelines, prompt workflows, tool-calling agents, and predictive models
 </li>
 <li>
 Build grounded retrieval over enterprise knowledge with source citation and tenant isolation
 </li>
 <li>
 Integrate models via the model gateway, applying guardrails, PII redaction, and content safety on every request
 </li>
 <li>
 Develop and maintain agent orchestration, memory, and human-in-the-loop escalation paths
 </li>
 <li>
 Perform data preprocessing, feature engineering, prompt design, and evaluation using enterprise datasets
 </li>
 <li>
 Deploy solutions through MLOps/LLMOps pipelines with monitoring, evaluations, and SLAs
 </li>
 <li>
 Optimise models and prompts for accuracy, latency, cost, and groundedness
 </li>
 <li>
 Run experiments, track metrics against golden sets, and iterate to improve quality
 </li>
 <li>
 Collaborate with AIOps and Security to integrate solutions into CI/CD and production monitoring
 </li>
 <li>
 Support responsible-AI practices, model cards, and version control for every release
 
 
 </li>
 </ul>
 
 About You
 
 
 
 6+ years in AI/ML engineering or applied machine learning
 
 
 Strong Python skills with scikit-learn, TensorFlow, PyTorch, or XGBoost, plus experience with LLM frameworks (LangChain/LangGraph) and RAG
 
 
 Experience with cloud AI services (AWS Bedrock/SageMaker, Azure, or GCP) and vector stores
 
 
 Proficiency in SQL and working with data warehouses/lakes and embeddings
 
 
 Familiarity with MLOps/LLMOps, containerisation (Kubernetes), and CI/CD
 
 
 Understanding of prompt engineering, evaluation harnesses, and guardrails
 
 
 Strong grasp of ML theory, software engineering practices, and version control (Git)
 
 
 
 Benefits
 
 
 
 <ul>
 <li>
 Competitive salary and incentive scheme
 </li>
 <li>
 Warm, supportive, and open company culture
 </li>
 <li>
 An opportunity to thrive in a global environment
 </li>
 <li>
 Hybrid working: 3 days in the office
 </li>
 <li>
 Birthday holiday and option to purchase additional annual leave
 </li>
 <li>
 Comprehensive Benefits Package: Private Pension, Private Medical Insurance, Life Assurance and more
 </li>
 </ul>
</div>

Senior Machine Learning Engineer

<div>
 
 
 Department:
 
 Data &amp; AI
 
 
 
 Location:
 
 UK - London
 
 
 
 
 Description
 
 
 
 Insurance isn&rsquo;t the first industry most engineers think of when they imagine cutting-edge AI work. That&rsquo;s exactly why this role is interesting.
 
 
 CFC&rsquo;s Data &amp; AI unit is building production agentic systems that automate complex underwriting decisions - not chatbots, not copilots bolted onto legacy workflows, but autonomous multi-step AI agents that reason over unstructured data, assess risk, and drive real business outcomes. We&rsquo;re using frameworks like LangChain to orchestrate LLM-driven services that sit at the heart of how the business operates. The problems are genuinely hard: ambiguous inputs, high-stakes decisions, and the kind of domain complexity that makes for satisfying engineering.
 
 
 We&rsquo;re also fundamentally rethinking how we deliver software. We&rsquo;re moving toward an agentic-first development model - using AI agents not just in what we build for the business, but in how we build it. The goal is to multiply engineering delivery by an order of magnitude: not by cutting corners or generating throwaway code, but by designing robust systems and processes that let agents handle well-defined work while engineers focus on architecture, design, and the problems that actually require human judgment. This is a deliberate, engineering-led approach. We care about code quality, testability, and maintainability - the agent-generated code meets the same standards as everything else. We expect that a successful candidate will be able to bring their expertise to help guide and refine our agentic development process as it matures - a meaningful opportunity to influence how we work as well as what we ship.
 
 
 This is a Senior Machine Learning / AI Engineer role with genuine technical leadership scope. You&rsquo;ll shape the architecture of AI-driven production microservices, own system design decisions, and work at the intersection of traditional software engineering and applied AI. You&rsquo;ll collaborate closely with engineers, data scientists, and product managers in a team that&rsquo;s small enough for your decisions to matter and ambitious enough for the work to stay interesting.
 
 
 
 About the role
 
 
 
 <ul>
 <li>
 Design, develop, and maintain business-critical AI agent services.
 </li>
 <li>
 Build tailored agent workflows and services from business requirements, using LangChain and related frameworks with reliable patterns for LLM-driven decision-making in production.
 </li>
 <li>
 Integrate tests and validation to improve AI agents through evals and monitoring.
 </li>
 <li>
 Work with software engineers and architects to lead system design and architectural decisions.
 </li>
 <li>
 Translate technical specifications into clean, testable, and scalable production code.
 </li>
 <li>
 Work closely with cross-functional teams - engineers, data scientists, product managers - to deliver features on time and to a high standard.
 </li>
 <li>
 Write unit and integration tests to maintain reliability and service correctness.
 </li>
 <li>
 Monitor, troubleshoot, and continuously improve production services.
 </li>
 <li>
 Produce clear, structured documentation for systems, architecture, and processes.
 </li>
 <li>
 Mentor junior engineers through code reviews, best-practice guidance, and knowledge sharing.
 
 
 </li>
 </ul>
 
 About you
 
 
 
 We&rsquo;re looking for an experienced AI or Machine Learning Engineer with strong software engineering fundamentals and a passion for building robust, production-ready AI systems. You&rsquo;ll be someone who enjoys solving complex technical problems, making thoughtful architectural decisions and working on systems where quality, reliability and maintainability really matter.
 
 
 You&rsquo;ll have:
 <ul>
 <li>
 Significant experience building production-grade AI agents or LLM-powered services.
 </li>
 <li>
 Strong Python development experience, ideally with 6+ years of professional software engineering experience.
 </li>
 <li>
 A track record of writing clean, maintainable and high-quality production code.
 </li>
 <li>
 Experience supporting business-critical systems in live production environments.
 </li>
 <li>
 Strong understanding of asynchronous programming, Docker, containerised deployments and modern service architectures.
 </li>
 <li>
 Experience designing distributed systems, asynchronous microservices and event-driven architectures.
 </li>
 <li>
 Confidence leading system design discussions and making pragmatic architectural trade-offs.
 </li>
 <li>
 Strong cloud experience, ideally within Azure.
 </li>
 <li>
 Experience deploying, monitoring and maintaining ML or LLM models in production.
 </li>
 <li>
 Familiarity with MLOps, LLMOps, evals, monitoring and lifecycle management.
 </li>
 <li>
 Hands-on experience orchestrating LLM workflows using frameworks such as LangChain.
 </li>
 <li>
 An interest in how agentic software development can improve engineering delivery without compromising code quality.
 </li>
 <li>
 Strong communication skills and the ability to collaborate effectively in remote or asynchronous environments.
 </li>
 <li>
 An ownership mindset, with the ability to work independently and contribute effectively to shared codebases.
 
 
 </li>
 </ul>
 
 Core Values
 
 
 
 
 Love what you do:
 
 
 We show up each day ready to take on the world. Our passion and intensity set us apart and makes the difference to our colleagues, customers, brokers and carriers.
 
 
 
 Challenge everything:
 
 
 We&rsquo;re never afraid to question the way that things are done and we constantly challenge ourselves and others to makes things better.
 
 
 
 Have fun, be good:
 
 
 Insurance is a serious business, but we don&rsquo;t take ourselves too seriously. We make it fun to work at CFC, we welcome all viewpoints, and we treat everyone how we would expect to be treated.
</div>

Salesforce

<div>To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Sales
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn&rsquo;t a buzzword &mdash; it&rsquo;s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You&rsquo;re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. 
D360 FORWARD DEPLOYED ENGINEER
<h1 class="jobSectionHeader">About Salesforce
</h1>Salesforce brings customer data, CRM, analytics, and AI together on a trusted platform. Our teams help organizations turn complex business and data requirements into secure, scalable solutions. In this role, you will help customers put Data 360 into production and build the technical foundation for reliable, enterprise-grade use cases.
<h1 class="jobSectionHeader">About the Role
</h1>Production implementation experience required. Builder mindset non-negotiable.
Salesforce is hiring Forward Deployed Engineers focused on Data 360, Salesforce&rsquo;s real-time data engine that unifies data from any source. As a Forward Deployed Engineer, you will be a hands-on Technical Builder who configures, builds, tests, and deploys Data 360 directly inside enterprise customer environments. You will turn approved solution designs and acceptance criteria into working implementations across ingestion, harmonization, identity, insights, activation, retrieval, security, and operational readiness.
Data 360 is also the data foundation for Agentforce - and grounding agents in accurate, real-time enterprise data is deep data engineering. You'll go further into RAG, vector search, MCP, and agent-to-agent integration than most data roles ever touch, because the agents are only as good as the layer you build.
You'll work within a delivery team that includes architects, deployment strategists, Agentforce FDEs, account teams, and customer technical teams. Engagements may span a focused proof, production rollout, and stabilization. The common thread is real customer data, disciplined testing, and a supportable handoff.
This is a hands-on data and software engineering role . You will build in real enterprise customer environments - writing SQL, Python, and Apex; building ingestion pipelines; debugging identity resolution; and shipping working implementations from sandbox through production. If you're looking for a purely declarative configuration role or a pure advisory role, this isn't it.
<h1 class="jobSectionHeader">What You'll Do
</h1><ul><li>Build in customer environments. Configure and develop directly in customer sandboxes and production - using Data 360 configuration, SQL, Apex, Flow, Python, the REST Query API, the Interaction SDK, Salesforce DX, CLI, Git, Data Kits, and deployment tooling as required - following security, change-management, and release controls.
</li><li>Engineer the data layer. Build ingestion, harmonization, and identity resolution across batch and streaming sources. Materialize views of data optimized for specific application, analytical, and agentic workloads, tuning latency and cost tradeoffs to meet use-case requirements. Work with customer data and AI teams to ensure implementations align with their existing strategies (e.g., data mesh, data fabric).
</li><li>Prove it works. Create representative test data and validate expected outcomes across happy paths, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer-scale volumes.
</li><li>Ground the agents. Design, build, and maintain the AI data integration layer - RAG (Retrieval-Augmented Generation), vector databases, search indexes, and knowledge bases - that grounds Agentforce solutions in accurate, real-time enterprise data.
</li><li>Orchestrate agent communication. Design and implement the protocols - including Model Context Protocol (MCP) and agent-to-agent communication - that govern, monitor, and ensure efficient collaboration among specialized AI agents.
</li><li>Connect the ecosystem. Implement robust, scalable, and secure data integration patterns that connect Agentforce to a wide range of enterprise applications and enable communication between AI agents.
</li><li>Govern the data. Apply deep data management expertise to ensure the secure integration, transformation, and governance of structured and unstructured data across the Salesforce ecosystem (CRM, Data Cloud) and external systems.
</li><li>Build for scale and security. Apply knowledge of message queues, event-driven architecture, and distributed systems to build resilient workflows and implement secure authentication and authorization protocols (OAuth, SAML) so that all agent actions are secure and comply with enterprise security policies.
</li><li>Debug the hard problems. Troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, query behavior, activation latency, API errors, permissions, and consumption issues - methodically, with logs and query evidence, isolating product behavior from configuration error.
</li><li>Own implementation decisions. Compare viable techniques within the approved architecture, document measured tradeoffs, and recommend the most maintainable implementation to the responsible architect or technical lead.
</li><li>Accelerate with AI. Use AI tooling - including Data 360 APIs and MCP Servers - to automate the build process and compress customers' time to value.
</li><li>Co-build and hand off. Work alongside customer technical teams and partners through pairing, code reviews, and configuration reviews. Package reusable metadata, scripts, queries, tests, and runbooks so the solution can be reproduced and supported. Support deployment, validation, production handoff, and early stabilization, leaving clear ownership and known limitations.
</li><li>Feed the roadmap. Surface reproducible platform gaps and edge cases - with live evidence, impact, and expected behavior - directly to Product and Support.
</li></ul>You're Our Data 360 Forward Deployed Engineer If...
<ul><li>You have 5+ years of experience in software engineering, data engineering, or technical implementation , including a production data solution you can explain in detail.
</li><li>You have a bachelor's degree in Computer Science or equivalent practical experience.
</li><li>You have hands-on experience implementing Salesforce Data 360 (Data Cloud) or a comparable enterprise data platform across ingestion, modeling, identity, insight, activation, and operations.
</li><li>You have hands-on experience building data pipelines and integrations using at least one enterprise data platform or technology, such as Snowflake, Databricks, BigQuery, Redshift, Kafka, cloud object storage, or an enterprise MDM platform.
</li><li>You are proficient in SQL and can build, debug, and optimize multi-table queries, joins, filters, transformations, and validation checks.
</li><li>You are fluent in at least one implementation language used in enterprise delivery - Apex, Java, Python, or JavaScript/TypeScript - and are willing to learn the others as needed.
</li><li>You understand data modeling, APIs, integration patterns, batch and streaming processing , and the practical differences between copied, federated, and Zero Copy data access.
</li><li>You've built retrieval systems for AI applications - vector databases, search indexes, embedding pipelines, or knowledge bases - and can explain the design decisions behind chunking, indexing, and relevance.
</li><li>You've implemented secure service-to-service integration patterns (OAuth, SAML, event-driven architectures) and are conversant in emerging agent protocols like MCP and agent-to-agent communication.
</li><li>You can implement secure data access and rigorously test it, including permissions, entitlements, consent, user context, restricted populations, and negative test cases .
</li><li>You diagnose technical problems methodically , use logs and query evidence, isolate product behavior from configuration errors, and document a reproducible result.
</li><li>You can implement against a documented architecture , explain what you built to customer technical teams, and recognize when a question requires an architect or security owner.
</li><li>You have delivered alongside customers, partners, professional services, or internal implementation teams in deadline-driven environments .
</li><li>You are excited to work directly with customer administrators, developers, data engineers, and architects.
</li><li>You have relevant certifications in Salesforce Agentforce, Data 360, and the Salesforce Platform. 
</li></ul>Unleash Your Potential
When you join Salesforce, you&rsquo;ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we&rsquo;ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future &mdash; but to redefine what&rsquo;s possible &mdash; for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates&rsquo; resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
We are a recognised Disability Confident member under the UK Government Disability Confident employer scheme. We are committed to providing an inclusive recruitment process and will offer an interview to disabled applicants who meet the essential criteria for the role. Applicants are welcome to opt-in to the interview scheme as part of the application process. If you would like to apply under the scheme, please click the link to the Accommodations Request Form above and scroll to the UK Disability Confident Scheme section within the form.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that&rsquo;s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications &ndash; without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.</div>

Data360 FDE

Distyl

<div>
 
 About Distyl AI
 
 
 
 Distyl is an applied AI technology company partnering with the world&rsquo;s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.
 
 
 We research and deploy technologies that power AI-native operations &mdash; both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations &mdash; from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.
 
 
 Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s.
 
 
 
 What We&rsquo;re Looking For
 
 
 
 We&rsquo;re opening an office in London, UK and looking for AI Engineers to design and deploy production-grade AI systems powered by LLMs.
 
 
 At Distyl, AI Engineers work directly with Fortune 500 companies to transform complex workflows using cutting-edge AI. You&rsquo;ll build and ship real-world AI applications &mdash; from intelligent agents to full-stack AI products &mdash; and see them operate at scale in mission-critical environments.
 
 
 This role is highly hands-on. You&rsquo;ll collaborate closely with customers, define system architectures, and build reliable, high-impact AI systems from prototype to production. Engineers at Distyl also help shape technical direction across major customer engagements, guiding enterprise teams through AI adoption and deployment.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Build Production AI Systems: Design, develop, and deploy robust AI applications using LLMs, including prompt engineering, agent workflows, tool use, and full-stack AI products
 </li>
 <li>
 Work Directly with Customers: Partner closely with enterprise stakeholders to understand complex problems and translate them into impactful AI solutions
 </li>
 <li>
 Lead System Architecture: Design scalable architectures for production AI systems, balancing performance, reliability, cost, and maintainability
 </li>
 <li>
 Develop Our Internal Platform: Contribute to Distillery, our internal LLM application platform, by building reusable infrastructure, tools, and workflows used across customer deployments
 </li>
 <li>
 Evaluate AI Systems Rigorously: Develop evaluation frameworks that measure model performance across accuracy, latency, cost, reliability, and safety
 </li>
 <li>
 Ship Production-Grade Systems: Ensure systems meet high standards for observability, reliability, security, and maintainability
 </li>
 <li>
 Raise the Engineering Bar: Improve development workflows, evaluation practices, and deployment strategies as our AI platform continues to evolve
 
 
 </li>
 </ul>
 
 Who You Are
 
 
 
 <ul>
 <li>
 3+ years of professional software engineering experience
 </li>
 <li>
 Strong proficiency in Python or TypeScript
 </li>
 <li>
 Experience building and deploying LLM-powered applications or AI agents in production
 </li>
 <li>
 Experience with modern LLM tooling such as LangChain, LlamaIndex, Guardrails, MCP, or agent frameworks
 </li>
 <li>
 Experience implementing RAG pipelines, tool use, or multi-step AI workflows
 </li>
 <li>
 Strong understanding of AI system evaluation, debugging, and observability
 </li>
 <li>
 Experience building reliable production systems with modern DevOps practices
 </li>
 <li>
 Experience deploying AI systems in enterprise environments is a plus
 </li>
 <li>
 Experience working across cloud platforms (AWS, GCP, or Azure) is a plus
 </li>
 <li>
 Experience with agent architectures and long-horizon task execution is a plus
 </li>
 <li>
 Familiarity with responsible AI practices, including auditability and governance is a plus
 
 
 </li>
 </ul>
 
 What We Offer
 
 
 
 <ul>
 <li>
 Competitive salary, meaningful equity, and a comprehensive benefits package
 </li>
 <li>
 Workplace Pension Scheme with employer contributions
 </li>
 <li>
 Private Medical Insurance (PMI) offered
 </li>
 <li>
 Flexible Time Off + Holidays
 </li>
 <li>
 Lunch provided on office days
 </li>
 <li>
 Access to state-of-the-art models, generous usage of modern AI tools, and real-world business problems
 </li>
 <li>
 Ownership of high-impact projects across top enterprises
 </li>
 <li>
 A mission-driven, fast-moving culture that prizes curiosity, pragmatism, and excellence
 
 
 </li>
 </ul>
 We believe diverse perspectives make our work stronger and more impactful. We are an equal opportunity employer and evaluate all applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other legally protected characteristic. We encourage candidates from all backgrounds to apply.
 
 
</div>

AI Engineer

Oliver Bernard

<div>
 
 🚀
 
 Staff AI Engineer | Stealth Mode AI Start-up | London | Up to &pound;200k
 
 
 
 
 
 
 Oliver Bernard have partnered with a
 
 stealth-mode start-up in the AI &amp; Research space
 
 , who are looking for a Staff AI Engineer to join the team and work on some genuinely challenging problems across LLMs, reinforcement learning and agentic systems.
 
 
 
 
 
 This is a hands-on research + engineering role, where you&rsquo;ll be involved in:
 
 
 
 
 
 🧠
 
 LLM training &amp; fine-tuning
 
 &ndash; including reinforcement learning and post-training
 
 
 🔄
 
 Evaluation &amp; feedback loops
 
 &ndash; building systems to continuously assess and optimise models
 
 
 🤖
 
 Agent development
 
 &ndash; designing and building sophisticated AI agents
 
 
 🔬
 
 AI research
 
 &ndash; exploring LLM training, RL, evaluation and optimisation techniques
 
 
 💻
 
 Software engineering
 
 &ndash; applying strong engineering fundamentals to complex AI problems
 
 
 
 
 
 
 What they&rsquo;re looking for:
 
 
 
 
 
 <ul>
 <li>
 Strong experience working with
 
 LLMs and/or foundation models
 
 </li>
 <li>
 Deep experience with
 
 training, fine-tuning, RL or post-training
 
 </li>
 <li>
 Strong background in
 
 model evaluation and optimisation
 
 </li>
 <li>
 Experience building
 
 AI agents / agentic systems
 
 </li>
 <li>
 Excellent software engineering fundamentals
 </li>
 <li>
 A strong track record in
 
 AI research
 
 and solving technically complex problems
 </li>
 </ul>
 
 
 
 
 📍
 
 London &ndash; 2 days hybrid
 
 
 
 💰
 
 Up to &pound;200k
 
 
 
 
 
 
 If you&rsquo;re a
 
 Staff-level AI Engineer
 
 who wants to work at the intersection of
 
 cutting-edge research and production engineering, apply below for more information.
 
 
 
 
 
 
 
 London | Stealth | Staff AI Engineer
 
 
</div>

Staff AI Engineer - Stealth Start-up - London

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