Lead Machine Learning Engineer (London)

Lead Machine Learning Engineer (London)

London Full-Time 72000 - 88000 £ / year (est.) Hybrid
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At a Glance

  • Tasks: Lead the development of innovative AI and machine learning solutions that drive business success.
  • Company: Join Fitch Group, a leader in credit ratings and analytics with a collaborative culture.
  • Benefits: Enjoy a competitive salary, excellent work-life balance, and opportunities for professional growth.
  • Other info: Be part of a diverse team where your ideas matter and career growth is encouraged.
  • Why this job: Make a real impact in the cutting-edge field of AI and machine learning.
  • Qualifications: 5+ years of experience in ML/AI engineering and proficiency in Python.

The predicted salary is between 72000 - 88000 £ per year.

The Fitch Group Emerging Technology AI Center of Excellence (COE) group is seeking a principal machine learning or AI engineer to be part of a team dedicated to building and supporting Generative AI, Machine Learning, Deep Learning, and Data Science solutions across the organisation. The position will be based out of our London or Manchester office.

Specific Emerging Technology team objectives are:

  • Gen AI/ML technology implementation with business and product owners
  • Emerging Tech Governance function covering policies, guidelines, and processes to govern AI/ML enabled components as well as third-party AI governance
  • Lead and support other enterprise-level AI exploration tools and capabilities
  • Provide guidance and support for safe development and deployment of AI

What We Offer:

  • This will be a high-impact role with significant visibility where the candidate will work on some flagship Fitch products.
  • The candidate will have an excellent opportunity to work in the cutting-edge field of machine learning and data science.
  • Fitch promotes an excellent work culture and is known for providing a good work-life balance.

We'll Count on You To:

  • Work closely as part of the product squads to build, integrate, and deploy GenAI, multi-AI-Agents, and data science solutions.
  • Ensure sharing best practices and learnings with other squad members.
  • Design and spearhead the development of Gen AI, AI Agents, and MLOps platforms, applications, and solutions driven by GenAI, deep learning, and machine learning to address business challenges and elevate product performance.
  • Build standards and recommendations for the AI engineering tech stack and data science models used across different enterprise AI use cases.
  • Work closely with AI implementation, governance, and enterprise architecture squads to apply the appropriate standards.
  • Keep abreast of the latest vendors across different areas of emerging technologies and actively participate in vendor evaluations and tool assessments for relevant use cases.
  • Where appropriate, onboard the right solutions into the platform.
  • Effectively communicate advanced data science/ML concepts in simple language to the business stakeholders, always focusing on its applicability to Fitch business.
  • Develop and deploy machine learning and Gen AI solutions to meet enterprise goals and support experimentation and innovation.
  • Collaborate with data scientists to identify innovative machine learning and Gen AI solutions that leverage data to meet business goals.
  • Design and develop scalable solutions and GenAI and ML workflows that leverage machine learning, Gen AI, and deep learning models to meet enterprise requirements.
  • Actively participate in maintaining SLAs for Production applications and support.
  • Create metrics to continuously evaluate the performance of machine learning solutions and improve the performance of existing machine learning solutions.
  • Build with AWS and Azure cloud computing services providing the necessary infrastructure, resources, and interfaces to enable data loading and LLM workflows.
  • Use Python/Java and large-scale data workflow orchestration platforms e.g. Airflow to construct software artifacts for ETL, interfacing with diverse data formats and storage technologies, and incorporate them into robust data workflows and dynamic systems.

What You Need to Have:

  • Work experience as a ML or AI engineer (5+ years).
  • Prior experience developing production quality Python is essential.
  • Deep understanding of Agentic AI systems and multi-agent architectures.
  • Proficiency in machine learning and deep learning algorithms such as Deep learning, NLP, Neural networks, multi-class classifications, decision trees, support vector machines.
  • Strong adherence to software and ML development fundamentals (e.g. code quality considerations, automated testing, source version control, optimisation).
  • Prior experience working on building Gen AI frameworks and leveraging and/or fine-tuning LLMs.
  • Experience building/enhancing search and information retrieval systems.
  • Exposure/experience in containerisation technologies like Docker, Kubernetes, AWS EKS etc.
  • Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g. AWS Bedrock, AWS S3, AWS Sagemaker, Azure AI search, Azure OpenAI, Azure blob storage etc.
  • Master's degree or above in Machine Learning/Data Science, Computer Science, Applied Mathematics or otherwise research-based field.

What Would Make You Stand Out:

  • Passionate about the power of data and predictive analytics to drive better business outcomes for customers.
  • Proven ability to work effectively in a distributed working environment and ability to work efficiently and productively in a fast-paced environment.
  • Familiarity with credit ratings agencies, regulations, and data products around the world.
  • Outstanding written and verbal communication skills.
  • A champion of good code quality and architectural practices.
  • Strong interpersonal skills and ability to work proactively and as a team player.

Why Fitch?

At Fitch Group, the combined power of our global perspectives is what differentiates us. Our global network of colleagues comes together to accomplish things greater than they ever could alone. Every team member is essential to our business and each perspective is critical to our success. We embrace a diverse culture that encourages a free exchange of ideas, guaranteeing your voice will be heard and your work will have an impact, regardless of seniority. We are building incredible things at Fitch and we invite you to join us on our journey.

Lead Machine Learning Engineer (London) employer: FitchGroup

Fitch Learning is an exceptional employer, offering a dynamic work environment in London that fosters professional growth and collaboration. With a strong commitment to employee development through dedicated training and mentorship programs, as well as a hybrid work model that promotes work-life balance, Fitch Learning empowers its team members to thrive in their careers while contributing to impactful learning solutions in the financial services sector. The inclusive culture and focus on wellbeing further enhance the experience, making it a rewarding place to work for those passionate about education and finance.

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Contact Details:

FitchGroup Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Lead Machine Learning Engineer (London)

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We think you need these skills to ace Lead Machine Learning Engineer (London)

Machine Learning
Deep Learning
Generative AI
Data Science
Python
Multi-Agent Architectures
Natural Language Processing (NLP)

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 FitchGroup.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at FitchGroup 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 FitchGroup

✨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 FitchGroup 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.