Senior Machine Learning Engineer

Immersum; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Immersum in London is seeking a Senior Machine Learning Engineer to own production-ready ML systems, deploy models, and build end-to-end pipelines. You will collaborate with a senior data scientist to turn notebooks into scalable services, APIs, and batch or streaming inference, ensuring reliability and cost efficiency.

You’ll shape the approach, work across the data platform, monitor drift, retraining triggers, and maintain feature pipelines, with emphasis on performance, latency, and

Qualifications

• 5+ years hands-on experience deploying and operating ML models in production.
• Proven experience building model-serving infrastructure, monitoring and retraining pipelines end to end.
• Strong software engineering skills to turn data science code into production-ready systems.
• Experience managing operational challenges of ML workloads in real-world environments.
• Working knowledge of cloud data warehouses, ideally Snowflake.
• Comfortable working with data scientists, making engineering trade-offs autonomously.

Responsibilities

• Build and maintain production ML deployment and serving infrastructure, including APIs and batch or streaming inference.
• Develop model versioning, experiment tracking and safe deployment processes.
• Implement monitoring for model performance and drift, including retraining triggers and alerting.
• Build and maintain feature engineering pipelines, ensuring training/production consistency.
• Translate data science models and notebooks into robust, production‑grade code.
• Optimise compute, latency and cost across training and inference workloads.
• Own CI/CD processes for model deployment and testing.
• Evaluate ML tooling and make pragmatic build‑versus‑buy decisions.
• Support the underlying data platform, including Snowflake administration and pipeline maintenance.
• Provide technical guidance to colleagues as the data function develops.

Skills

ML deployment

Model serving infrastructure

CI/CD for ML

Python

Snowflake

Cloud data warehouses

Monitoring & retraining pipelines

Tools

Snowflake Cortex

Cortex Code

MLFlow

APIs

Job description

Job Title: Senior Machine Learning Engineer

Location: London – Hybrid (3 days p/w in office)

Industry: Green Tech, Clean Energy

Tech: Python, Snowflake, Snowflake Cortex/Cortex Code, MLFlow

The Company:

We’re supporting a fast-growing business in the clean energy space, playing a key role in the UK’s transition to sustainable transport. With data and AI becoming increasingly important to its commercial and operational strategy, the business is investing in its machine learning capability and looking for someone who can take models out of the notebook and into production.

The Role

This is a hands‑on Senior ML Engineering role focused on building, deploying and operating machine learning models in production. Working closely with a senior data scientist, you’ll take ownership of the engineering that sits around the models, from deployment infrastructure and inference pipelines to monitoring, retraining and ongoing optimisation. You’ll have real autonomy to shape the approach, make pragmatic technical decisions and ensure models are reliable, scalable and cost‑effective. The team is small, so you’ll also need to be comfortable working across the data platform when required, without relying on a dedicated platform engineering function.

Key Responsibilities

• Build and maintain production ML deployment and serving infrastructure, including APIs and batch or streaming inference
• Develop model versioning, experiment tracking and safe deployment processes
• Implement monitoring for model performance and drift, including retraining triggers and alerting
• Build and maintain feature engineering pipelines, ensuring consistency between training and production
• Translate data science models and notebooks into robust, production‑grade code
• Optimise compute, latency and cost across training and inference workloads
• Own CI/CD processes for model deployment and testing
• Evaluate ML tooling and make pragmatic build‑versus‑buy decisions
• Support the underlying data platform, including Snowflake administration and pipeline maintenance
• Provide technical guidance to colleagues as the data function develops

What We’re Looking For

• 5+ years’ hands‑on experience deploying and operating ML models in production
• Proven experience building model‑serving infrastructure, monitoring and retraining pipelines end to end
• Strong software engineering skills, with the ability to turn data science code into maintainable, production‑ready systems
• Experience managing the operational challenges of running ML workloads in real‑world environments
• Working knowledge of cloud data warehouses, ideally Snowflake
• Comfortable working closely with data scientists, challenging assumptions and making sound engineering trade‑offs
• A pragmatic, autonomous approach, suited to a small team without dedicated platform engineering support

Experience with Snowflake Cortex, Cortex Code or similar in‑warehouse ML tooling would be beneficial, as would mentoring less experienced engineers or analysts.

Why This Role

• Take ownership of getting machine learning models into production and keeping them there
• Work closely with a senior data scientist to turn models into real‑world applications
• Have genuine influence over ML infrastructure, tooling and engineering decisions
• Join a growing business operating at the intersection of technology, EV infrastructure and clean energy
• Enjoy the autonomy and variety of working in a small team where your engineering decisions have a direct impact