At a Glance
- Tasks: Lead the design and delivery of advanced AI solutions for a major enterprise client.
- Company: Global technology consultancy with a focus on innovation and collaboration.
- Benefits: Hybrid work model, competitive pay, and opportunities for professional growth.
- Other info: Dynamic role with potential for contract extension and career advancement.
- Why this job: Join a cutting-edge team and shape the future of AI technology.
- Qualifications: 8-12+ years in data engineering or AI, strong Python and SQL skills required.
The predicted salary is between 81000 - 99000 £ per year.
Contract Duration: 6 Months (with view to extend) Inside IR35
Location: London (Canary Wharf) — Hybrid (3 days onsite / 2 days remote)
A global technology consultancy is seeking a Principal AI Data Engineer to spearhead the design, architecture, and production delivery of advanced Generative and Agentic AI solutions for a major enterprise client. In this role, you will bridge the gap between rapid frontier AI experimentation and robust, large-scale enterprise data engineering. Working closely with commercial stakeholders, cross-functional squads, and technical leadership, you will translate high-value operational use cases into reliable, production-grade AI pipelines, intelligent agentic workflows, and governed data platforms.
Key Responsibilities
- Agentic AI & Prototype Delivery: Architect, build, and deploy multi-agent workflows and task-driven AI systems using frameworks such as LangGraph, LangChain, or AutoGen, turning early proof-of-concepts into resilient production applications.
- Retrieval-Augmented Generation (RAG): Design and tune enterprise-grade RAG pipelines, including chunking strategy, dense/hybrid retrieval, reranking, and semantic search.
- Data Platform Engineering: Build scalable, streaming, and batch data processing pipelines across modern cloud lakehouse architectures (Delta Lake, streaming architectures, and unified governance layers).
- Systematic Evaluation & Benchmarking: Establish continuous model evaluation, reproducibility checks, tracing, and benchmarking frameworks (MLflow, LLM-as-a-judge, golden test suites) to ensure performance, safety, and auditability.
- Architecture & Backlog Leadership: Set technical standards, define architectural blueprints, shape the product backlog, and lead agile delivery squads while communicating technical trade-offs to commercial business stakeholders.
- Production MLOps & Governance: Implement modern software engineering and CI/CD practices (OOP in Python, Pydantic, containerisation, automated testing, and cloud gateway governance).
Key Technical Stack & Experience
- Experience: 8–12+ years of hands-on experience in data engineering, data science, or production ML/AI environments.
- Agentic Frameworks: Proven delivery with LangGraph, AutoGen, CrewAI, smolagents, or Model Context Protocol (MCP) integrations.
- Lakehouse & Data Engineering: Strong background with enterprise Databricks environments (Delta Lake, Unity Catalog, Delta Live Tables/DLT, Spark Structured Streaming, or Autoloader).
- Cloud & Model Ecosystem: Broad exposure to Azure cloud environments (Azure AI Foundry, Azure OpenAI) or Databricks Mosaic AI ecosystem.
- Code Craftsmanship: Advanced Python (modular OOP, Pydantic, design patterns) and strong SQL capabilities.
- Education: Degree or advanced degree (BSc / MSc) in Mathematics, Statistics, Computer Science, Engineering, Econometrics, or related quantitative discipline.
Principal AI Engineer in London employer: Talent
Join a leading global hospitality brand as a Field Tech Analyst in Watford, where you will be part of a dynamic EMEA HR Technology team driving innovative HR solutions. The company fosters a collaborative work culture that values employee growth and offers extensive training opportunities, ensuring you can develop your skills while contributing to impactful projects. With a hybrid working model, you can enjoy the flexibility of working both onsite and remotely, making it an ideal environment for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Principal AI Engineer in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Talent or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Talent.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Talent.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Talent that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Principal AI Engineer in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Talent.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Talent and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Talent
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Talent uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
