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For Companies At a Glance
- Tasks: Engineer cutting-edge AI systems and develop decision-making loops for intelligent tools.
- Company: Innovative tech firm leading the charge in AI development.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Why this job: Join a team pushing the boundaries of AI and make a real impact.
- Qualifications: 5+ years in ML, strong coding skills, and experience with production models.
- Other info: Dynamic environment with a focus on innovation and collaboration.
The predicted salary is between 48000 - 72000 ÂŁ per year.
We are building the next generation of intelligence. This isn’t just about calling an API; it’s about engineering the plumbing, the memory, and the reasoning logic that allows AI to navigate complex datasets. You will be responsible for delivering fast, brilliant, and architecturally sound solutions.
What You’ll Own:
- Cognitive Architecture: Beyond simple prompts, you will engineer the decision‑making loops (agents) that allow our tools to self‑correct and execute multi‑step coding tasks.
- Context Engineering: Develop the retrieval and embedding logic that ensures the model “sees” the right data at the right time, minimizing noise and maximizing signal.
- System Integrity: Move beyond “vibe‑based” testing. You’ll build rigorous, automated frameworks to quantify model behaviour and prevent regressions in production.
- Model Lifecycle: Own the decision between fine‑tuning a specialised small model versus orchestrating a frontier LLM, balancing latency with reasoning depth.
- Technical Leadership: Act as the “Engineer’s Engineer,” setting the standard for how we write production‑grade ML code and mentor the team on high‑stakes delivery.
Your Technical Toolkit:
- The GenAI Stack: Extensive experience with the “Agentic” ecosystem (orchestration frameworks, vector‑native databases, and semantic search).
- Production ML: A history of shipping models that actually handle traffic. You know that “done” means deployed, monitored, and stable.
- Code‑Fluent: You are a strong software engineer. You are as comfortable in the depths of a Python backend as you are tweaking a model’s temperature. Familiarity with JVM‑based languages (Java/Kotlin) is a significant edge.
- The Scientific Method: You don’t guess; you experiment. You have a background in statistical validation and know how to prove a model’s value via data.
Why You’re a Fit:
- You find the “unknowns” of Agentic AI exciting, not paralyzing.
- You believe that a model is only as good as the data pipeline feeding it.
- You are tired of “wrapper” apps and want to build deep, integrated AI systems.
- You have 5+ years of total ML experience, with a heavy recent focus on the LLM frontier.
Senior Machine Learning Systems Engineer in London employer: iForce Connect
Contact Detail:
iForce Connect Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Systems Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 projects, especially those related to machine learning systems. 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 technical concepts and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and approach complex problems!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team and contributing to the next generation of intelligence.
We think you need these skills to ace Senior Machine Learning Systems Engineer in London
Some tips for your application 🫡
Show Your Passion for AI: When 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 building the next generation of intelligence.
Tailor Your Experience: Make sure to highlight your relevant experience in cognitive architecture and production ML. We’re looking for specific examples that demonstrate how you’ve tackled complex datasets and built robust solutions in the past.
Be Clear and Concise: While we love detail, clarity is key! Keep your application straightforward and to the point. Use bullet points where possible to make it easy for us to see your skills and achievements at a glance.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at iForce Connect
✨Know Your Tech Inside Out
Make sure you’re well-versed in the GenAI stack and can discuss your experience with orchestration frameworks, vector-native databases, and semantic search. Be ready to share specific examples of how you've shipped models that handle traffic and what challenges you faced.
✨Showcase Your Problem-Solving Skills
Prepare to discuss how you approach complex datasets and decision-making loops. Think of a few scenarios where you’ve had to engineer solutions that self-correct or execute multi-step coding tasks, and be ready to explain your thought process.
✨Demonstrate Your Leadership Qualities
As a Senior Machine Learning Systems Engineer, you’ll need to show that you can mentor others and set high standards. Share experiences where you’ve led a project or team, focusing on how you ensured production-grade ML code was written and maintained.
✨Emphasise Your Experimental Mindset
Highlight your background in statistical validation and how you use the scientific method in your work. Be prepared to discuss how you’ve proven a model’s value through data and what experiments you’ve conducted to refine your approaches.
