At a Glance
- Tasks: Design, build, and deploy cutting-edge GenAI applications from concept to production.
- Company: Join a major enterprise driving AI transformation in Central London.
- Benefits: Competitive pay, flexible work schedule, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on full-stack ML engineering.
- Why this job: Make a real impact by working on high-scale, innovative AI projects.
- Qualifications: Hands-on experience with GenAI applications and strong Python skills required.
The predicted salary is between 60000 - 80000 € per year.
Machine Learning Engineers – GenAI Application Development (End‑to‑End) supporting a major enterprise AI transformation programme.
12 month contract, Inside IR35, 2 days per week onsite - Central London.
I’m looking for Machine Learning Engineers (and Senior Data Scientists with strong engineering capability) who have real, hands‑on experience building GenAI applications end‑to‑end. Not just prototypes, but production systems.
This is a full‑stack ML Engineering role where you’ll design, build, deploy, and monitor GenAI applications that integrate LLMs as part of a wider system.
You’ll be working on:
- Building production‑grade GenAI applications using Python
- Developing LLM‑powered systems with LangChain, LangSmith, and modern Python libraries
- Designing and implementing RAG pipelines, vector stores, and retrieval logic
- Building agentic workflows, tool‑calling, and multi‑step reasoning systems
- Implementing guardrails, safety layers, and controls
- Designing monitoring for latency, drift, hallucinations, cost, and safety signals
- Integrating LLMs into broader application architectures (APIs, services, orchestration)
- Working across the full lifecycle: data prep → modelling → evaluation → deployment → observability
We’re looking for people who have:
- Delivered real GenAI applications into production, not just PoCs
- Strong Python engineering skills
- Experience with LangChain, LangSmith, LlamaIndex, or similar frameworks
- Deep understanding of LLM behaviour, prompting, evaluation, and optimisation
- Experience building monitoring, logging, and guardrail frameworks
- Ability to work across the stack: data, model, application, and infrastructure
- Strong communication skills and ability to work with product, engineering, and business teams
This is ideal for ML Engineers who love building end‑to‑end systems, not just models. People who can take a GenAI idea from concept to production and own the full lifecycle. If you’ve built and shipped GenAI applications and want to work on high‑impact, enterprise‑scale projects, please apply.
Machine Learning Engineer in London employer: Puritas Group
Join a forward-thinking company at the forefront of AI transformation, where as a Machine Learning Engineer, you will have the opportunity to work on high-impact projects in a collaborative and innovative environment. With a strong emphasis on employee growth, we offer continuous learning opportunities and a supportive culture that values creativity and technical excellence. Located in Central London, enjoy the vibrant city life while contributing to cutting-edge GenAI applications that make a real difference.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and machine learning space. Attend meetups, webinars, or even local tech events. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your GenAI projects. Include links to GitHub repos or live demos. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with Python, LangChain, and building production systems. Practice explaining complex concepts in simple terms – it shows you can communicate effectively with non-technical teams.
✨Tip Number 4
Don’t just apply anywhere; focus on companies that excite you! Use our website to find roles that match your skills and interests. Tailor your approach to each application, showing how your experience aligns with their needs.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Experience:When you're writing your application, make sure to highlight your hands-on experience with GenAI applications. We want to see real examples of production systems you've built, not just prototypes. This is your chance to shine!
Tailor Your Application:Don’t just send a generic application! Tailor it to the job description by using keywords and phrases that match what we’re looking for. This shows us you’ve done your homework and are genuinely interested in the role.
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that get straight to the heart of your skills and experiences. Avoid fluff and focus on what makes you a great fit for the Machine Learning Engineer role.
Apply Through Our Website:Make sure to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do – just follow the prompts and you’ll be all set!
How to prepare for a job interview at Puritas Group
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and frameworks like LangChain and LangSmith. Brush up on your understanding of LLMs and how they integrate into applications, as this will likely come up during technical discussions.
✨Showcase Real Projects
Prepare to discuss specific GenAI applications you've built and deployed. Highlight your role in taking these projects from concept to production, focusing on the challenges you faced and how you overcame them. This will demonstrate your hands-on experience and problem-solving skills.
✨Communicate Clearly
Strong communication skills are key for this role. Practice explaining complex technical concepts in simple terms, as you’ll need to collaborate with product and business teams. Be ready to discuss how you’ve worked cross-functionally in past projects.
✨Prepare for Scenario Questions
Expect scenario-based questions that assess your ability to design and implement systems. Think about how you would approach building a production-grade GenAI application, including considerations for monitoring, safety layers, and performance metrics. This will show your strategic thinking and full-stack capabilities.
