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- Tasks: Own the ML model lifecycle and build cutting-edge AI features.
- Company: Join Boam AI, a dynamic team transforming data into intelligence.
- Benefits: Top-tier compensation, equity options, and high autonomy.
- Why this job: Make a real impact on mission-critical AI systems for leading enterprises.
- Qualifications: 3+ years in ML engineering with strong Python skills.
- Other info: Fast, transparent hiring process with direct founder interaction.
The predicted salary is between 36000 - 60000 £ per year.
Ship production ML and agentic AI powering market leaders worldwide. Boam AI builds managed data solutions that transform messy, unstructured signals from public, private, and proprietary sources into structured, reliable, and always up-to-date intelligence on millions of SMBs and enterprises worldwide. These agentic systems power CRMs, data warehouses, AI products, and mission-critical decisions across the enterprise.
As an Applied AI Engineer, you will own the lifecycle of the models and agents that power our product. You will build and maintain ML data pipelines, develop production-ready LLM- and agent-driven features, and work closely with backend engineers to integrate models into real systems. This is a role for someone who has shipped ML to production, wants real ownership over models and pipelines, and is excited to work on a small, senior team where AI is at the core of the product.
What You’ll Do
- Own the ML model lifecycle from training and evaluation to deployment
- Build and maintain ML & agentic data pipelines for training, inference, and monitoring
- Develop production-ready agentic and large-language-model–driven features
- Integrate models into production systems in close collaboration with backend engineers
- Implement experiment tracking, model CI/CD, and automated retraining
- Improve performance, reliability, and observability of ML and AI systems in production
- Work with product and data teams to turn ambiguous problems into concrete ML/AI solutions
- Use next-gen AI tools to improve iteration speed and model quality
You Might Be a Fit If…
- 3+ years of ML engineering experience working on production systems
- Strong Python skills and hands-on ownership of end-to-end ML pipelines or agentic systems
- Comfortable with data preprocessing, feature engineering, and evaluation at scale
- Familiarity with LLMs or modern foundation models
- Experience with common ML tooling (e.g. PyTorch, TensorFlow, XGBoost, vector DBs, experiment trackers)
- Comfortable working across APIs, data stores, and infrastructure, not just notebooks
- Bias to ship, measure, and refine rather than chase perfect offline metrics
- Motivated by solving real customer problems and seeing models used in the wild
- Thrive without heavy process, QA buffers, or endless safeguards – you own what you ship
Why Boam AI
- Join a no-politics, high-trust, low-ego, and high-talent team
- Work on mission-critical ML/AI systems used by top-tier enterprise customers
- Work directly with founders, the Head of Engineering, and senior engineers on problems that matter
- High autonomy, real impact, and clear ownership from day one
- Operate at the intersection of AI, data infrastructure, and enterprise workflows
- Top-tier compensation with meaningful equity upside
- Help shape the ML/AI platform, patterns, and practices you can be proud of
Our Hiring Process
- Intro Call
- Deep Dive
- Work Sample
- Founder / Leadership Conversation
Candid discussion with our founder and Head of Engineering on ambition, values, ownership, and how you would help us scale our ML and agentic systems.
Applied AI Engineer in London employer: Boam AI
Contact Detail:
Boam AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML space, especially those who work at Boam AI or similar companies. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving production systems. Share it on platforms like GitHub and make sure it's easy for us to see what you've done.
✨Tip Number 3
Prepare for the interview by diving deep into our tech stack. Brush up on Python, ML pipelines, and tools like PyTorch or TensorFlow. We love candidates who can hit the ground running and speak our language!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining our team and being part of the exciting work we do at Boam AI.
We think you need these skills to ace Applied AI Engineer in London
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI and machine learning shine through. We want to see that you’re not just ticking boxes but genuinely excited about the work we do at Boam AI.
Tailor Your Experience: Make sure to highlight your relevant experience with ML pipelines and agentic systems. Use specific examples from your past roles to demonstrate how you've owned the lifecycle of models and contributed to production systems.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and focus on what makes you a great fit for the Applied AI Engineer role. Remember, less is often more!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the hiring process.
How to prepare for a job interview at Boam AI
✨Know Your ML Models Inside Out
Make sure you can discuss the lifecycle of ML models in detail. Be prepared to explain your experience with training, evaluation, and deployment, as well as any specific projects where you've taken ownership of these processes.
✨Showcase Your Python Proficiency
Since strong Python skills are crucial for this role, be ready to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so brush up on your Python syntax and common libraries like PyTorch or TensorFlow.
✨Prepare for Real-World Problem Solving
Think about how you've tackled ambiguous problems in the past. Be ready to share examples of how you've turned vague challenges into concrete ML/AI solutions, especially in production environments.
✨Emphasise Collaboration with Engineers
This role involves working closely with backend engineers, so highlight your experience in cross-functional teams. Discuss how you've integrated models into production systems and the importance of communication in those collaborations.
