Machine Learning Engineer (Remote)
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Job description
| Machine Learning Engineer | Production AI & LLM Systems |
If you’re an ML Engineer who enjoys building, deploying and scaling AI systems in production, rather than just experimenting with models, this could be a great fit.
You’ll work across ML Engineering, Software Engineering and Cloud Infrastructure, with real ownership over how AI services are built, deployed and operated.
Why it’s interesting:
- Build and scale production AI/LLM systems, including RAG and agentic workflows
- Own the full engineering lifecycle, from Python development through to AWS deployment
- Work with Python, FastAPI, AWS, Docker, CI/CD and infrastructure-as-code
- Solve real engineering challenges around reliability, observability, performance and cost
- Influence technical decisions, improve engineering practices and grow towards Senior/Lead ownership
- Remote flexibility with optional office access
What we’re looking for:
- Strong Python engineering skills and experience building production services
- Hands-on AWS, CI/CD, containers and cloud infrastructure experience
- Experience deploying, monitoring and maintaining production ML/AI systems
- Exposure to LLMs, RAG or GenAI applications
- An engineer who enjoys debugging complex systems and taking end-to-end ownership
Ideally 4+ years’ experience, but technical depth and production engineering experience matter more than tenure.
This could suit someone coming from ML Engineering, MLOps, Platform Engineering or backend Software Engineering who wants to work closer to production AI.
Requirements
- Strong Python engineering skills and experience building production services
- Hands-on AWS, CI/CD, containers and cloud infrastructure experience
- Experience deploying, monitoring and maintaining production ML/AI systems
- Exposure to LLMs, RAG or GenAI applications
- An engineer who enjoys debugging complex systems and taking end-to-end ownership
Ideally 4+ years’ experience, but technical depth and production engineering experience matter more than tenure.
This could suit someone coming from ML Engineering, MLOps, Platform Engineering or backend Software Engineering who wants to work closer to production AI.
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