At a Glance
- Tasks: Build and optimise AI solutions using cutting-edge technologies and frameworks.
- Company: Join EPAM, a leading tech company in London with a hybrid work culture.
- Benefits: Enjoy competitive pay, health insurance, and perks like free lunches and social events.
- Other info: Great opportunities for learning, growth, and collaboration across teams.
- Why this job: Make an impact in AI while working on innovative projects in a dynamic environment.
- Qualifications: Strong Python skills and experience with API development and AI frameworks.
The predicted salary is between 60000 - 80000 £ per year.
We're looking for a Machine Learning Engineer to join EPAM in London, United Kingdom, in a hybrid working mode. You will contribute to an enterprise AI program for one of our clients by building and surfacing MCP Servers and Tools via the internal MCP Gateway and implementing evaluation mechanisms for large language models and agent-based systems. This is a highly technical role combining backend engineering, AI architecture, and enterprise integration to deliver scalable AI solutions.
As an AI Engineer, you will work on intelligent enterprise enablement through Model Context Protocol (MCP) integrations and advanced agent frameworks. The role involves API development with FastAPI, deployment of containerized systems, and implementing large language model (LLM) strategies such as prompt engineering and RAG (Retrieval-Augmented Generation). The successful candidate will operate in a Classic Agile environment to design, develop, and optimize next-generation enterprise AI capabilities.
Responsibilities- Build and surface MCP Servers and Tools using the internal MCP Gateway
- Develop APIs and service endpoints with FastAPI for MCP integrations
- Implement agent and LLM frameworks (e.g., LangChain, LangGraph) to enable sophisticated AI workflows
- Apply prompt engineering strategies and build RAG-based architecture for enhanced response accuracy
- Automate quality controls and develop evaluation systems for LLM reliability and performance
- Containerize and orchestrate AI services using Kubernetes for scalable deployment
- Collaborate cross-functionally with product, QA, and architecture teams to ensure secure, enterprise-grade solutions
- Maintain documentation for MCP integrations, testing pipelines, and workflow standards
- Integrate CI/CD practices to streamline deployment and testing processes
- Strong programming experience in Python for backend development and automation
- Expertise in REST API development using FastAPI for production-scale services
- Familiarity with Model Context Protocol (MCP) standards, server/client patterns
- Working knowledge of LLM/agent frameworks such as LangChain or LangGraph
- Understanding of containerization and orchestration tools like Kubernetes
- Ability to implement prompt engineering principles and RAG-based solutions
- Solid grasp of Agile delivery models, CI/CD workflows, and secure coding practices
- Excellent problem-solving and cross-team communication skills
- Experience with observability and monitoring tools for AI-driven workloads
- Familiarity with vector databases for semantic search and retrieval
- Knowledge of cloud platforms (AWS, GCP, Azure) and GPU-accelerated compute
- Background designing evaluation frameworks for AI model performance
- Exposure to large-scale AI deployment in enterprise environments
- EPAM Employee Stock Purchase Plan (ESPP)
- Protection benefits including life assurance, income protection and critical illness cover
- Private medical insurance and dental care
- Employee Assistance Program
- Cyclescheme, Techscheme and season ticket loans
- Various perks such as free Wednesday lunch in-office, on-site massages and regular social events
- Learning and development opportunities including in-house training and coaching, professional certifications, and courses
- If otherwise eligible, participation in the discretionary annual bonus program
- If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
Machine Learning Engineer employer: EPAM Systems
EPAM is an exceptional employer for Machine Learning Engineers, offering a dynamic hybrid working environment in London that fosters innovation and collaboration. With a strong focus on employee growth, we provide extensive learning and development opportunities, including in-house training and professional certifications, alongside attractive benefits such as private medical insurance and a unique Employee Stock Purchase Plan. Our vibrant work culture encourages creativity and teamwork, making it an ideal place for those looking to make a meaningful impact in the field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the field. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects, especially those involving Python, FastAPI, or any LLM frameworks. We love seeing real examples of your work, so make sure to highlight your best stuff when you get the chance.
✨Ace the Interview
Prepare for technical interviews by brushing up on your coding skills and understanding of AI concepts. Practice common interview questions related to backend development and containerization. We want to see how you think and solve problems, so be ready to showcase your thought process!
✨Apply Through Our Website
When you find a role that excites you, apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates who are eager to contribute to our innovative projects.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with Python, FastAPI, and any relevant AI frameworks. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects:If you've worked on any projects related to LLMs or containerization, make sure to mention them! We’re keen to see real examples of your work, so don’t hold back on sharing your achievements and what you learned.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing applications come directly from our site!
How to prepare for a job interview at EPAM Systems
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and get comfortable with FastAPI. Be ready to discuss your experience with REST API development and how you've tackled backend challenges in the past. The more specific examples you can provide, the better!
✨Showcase Your AI Knowledge
Familiarise yourself with Model Context Protocol (MCP) standards and frameworks like LangChain or LangGraph. Be prepared to explain how you've implemented prompt engineering strategies or RAG-based architectures in previous projects. This will show that you’re not just a coder but also an AI enthusiast.
✨Demonstrate Agile Experience
Since this role operates in a Classic Agile environment, be ready to discuss your experience with Agile methodologies. Share examples of how you've collaborated with cross-functional teams and integrated CI/CD practices into your workflow. This will highlight your adaptability and teamwork skills.
✨Ask Insightful Questions
Prepare some thoughtful questions about the company's AI initiatives and the specific projects you'll be working on. This shows your genuine interest in the role and helps you gauge if the company culture aligns with your values. Plus, it’s a great way to engage with your interviewers!
