Machine Learning Engineer

Techmunity; United Kingdom; Remote; GBP 110,000 - 130,000; Full time

Benefits offered by this job

Equity

Fully remote (UK residents)

Job summary

Techmunity is seeking a Forward Deployed ML Engineer to deliver production-ready ML services for a physics-grounded energy platform. You’ll deploy APIs, containerised workloads, and RAG systems, collaborating with engineers and process experts.

You have a track record of moving ML models into production, strong Python and DL skills (PyTorch), and experience with Docker and Kubernetes. Compensation ranges £110k–£130k + equity, fully remote for UK residents.

Qualifications

• Track record of taking ML models from development into production
• Experience building LLM or RAG systems with data quality and retrieval considerations
• Strong Python and deep learning (PyTorch) with FastAPI or similar ML services

Responsibilities

• Deploy APIs and containerised workloads for production ML systems
• Improve RAG and retrieval workflows to ensure data reliability
• Collaborate with process engineers and data/FPGA teams to resolve production issues

Skills

Production ML

Python & Deep Learning

Time-series data

LLM systems

Tools

Docker deployment

Kubernetes

Job description

Fully remote, UK | Occasional customer travel to Europe/Africa

Are you an experienced ML Engineer who wants to build technology that reduces energy waste and carbon emissions in the industries that keep the world running?

This Series A AI company has built a physics-grounded platform for energy operations, helping industrial sites make safer, faster and more efficient decisions from their operational data. They've recently raised $20m and are scaling deployments with global energy operators.

As a Forward Deployed ML Engineer, you’ll work in a small pod with Full Stack and Data Engineers to make the core platform work in live customer environments.

You’ll take ML systems from model development into trusted production services, deploying APIs and containerised workloads, tuning time-series and anomaly-detection models, and improving RAG and agent workflows where retrieval quality, data quality or reliability falls short.

You’ll work directly with process engineers, solve difficult production issues and feed customer learning back into the product.

You’ll be supported by leaders spanning Imperial AI research, Shell’s global AI programme and 25 years of real-time energy data and AI deployments.

You’ll need:

• A track record of taking ML models from development into reliable production services, including API design, Dockerised deployment and Kubernetes or similar orchestration
• Practical experience building LLM or RAG systems, with an understanding of the real challenges around data quality, chunking, retrieval precision, evaluation and production reliability
• Experience working with complex data, particularly time series, 2D/3D or other high-dimensional datasets, and the judgement to explain why a particular modelling approach was appropriate
• Strong Python and deep learning experience, ideally PyTorch, plus FastAPI or similar frameworks for ML services
• The ability to explain model behaviour and technical trade-offs clearly to engineers, customers and domain experts

This Forward Deployed ML Engineer role is paying between £110,000 to £130,000 + equity depending on experience, and is fully remote for UK residents. No VISA sponsorship available.