Cognify Search
Senior Machine Learning Engineer
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Senior ML Engineer
Up to £110k + bonus
Location: London (Hybrid)
I’m currently working with a leading UK consumer technology business that is investing heavily in Machine Learning, AI, and LLM-powered products.
They’re looking for a Senior Machine Learning Engineer to play a key role in taking ML and AI capabilities from experimentation into reliable, scalable production.
This is a hands-on engineering role with significant technical scope. You’ll work closely with Data Scientists, Software Engineers, and Product teams, helping shape architecture, build production systems, and develop the tooling and standards that allow ML and AI initiatives to scale.
What you’ll be doing
- Own the end-to-end delivery of production ML and AI solutions, working closely with Data Science and Product teams.
- Design and build robust pipelines for model training, validation, and deployment.
- Develop model packaging, deployment, and lifecycle automation using modern tooling.
- Build monitoring and observability capabilities covering model performance, drift, reliability, and operational health.
- Work across both batch and real-time ML workloads.
- Help take emerging LLM and AI capabilities into production, including RAG, tool use, and agentic workflows.
- Contribute to the evolution of the internal ML/AI platform, improving experimentation, governance, reproducibility, and collaboration.
- Build reusable tools and libraries that improve the speed and quality of ML development.
- Establish best practices around testing, CI/CD, model observability, evaluation, and governance.
- Provide technical leadership through architecture discussions, design reviews, code reviews, and mentoring.
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What they’re looking for
- Strong hands-on experience building and deploying ML models into production.
- Excellent Python and software engineering skills, including APIs and scalable production services.
- Experience with LLM-based systems, such as prompt engineering, RAG, tool use, or orchestration frameworks such as LangChain or LangGraph.
- Experience building multi-step AI systems where models can plan, retrieve information, and take actions.
- Experience with modern ML/MLOps tooling such as Databricks, MLflow, Airflow, Kubeflow, SageMaker, or Vertex AI.
- Strong understanding of ML lifecycle management, including versioning, testing, monitoring, and governance.
- Experience with cloud-native environments, CI/CD, and infrastructure-as-code such as Terraform or CloudFormation.
- A strong understanding of building maintainable, testable, and scalable ML pipelines and APIs.
- Excellent communication skills and the ability to work effectively across Data Science, Engineering, and Product.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
If you're an ML Engineer who enjoys getting beyond experimentation and actually building, deploying, and scaling AI systems in production, I'd love to hear from you.
📩 Reach out to daniel@cognifysearch.com
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