Senior Machine Learning Engineer

Cognify Search; Greater London; On-site; GBP 90,000 - 150,000; Full time

Job summary

Cognify Search is seeking a Senior Machine Learning Engineer to scale ML capabilities from experimentation into reliable production in a hands-on, production-focused role in the UK. You will collaborate with Data Scientists, Software Engineers and Product teams to shape architecture, build production systems and create tooling that scales ML initiatives.

The role emphasizes end-to-end delivery, robust pipelines, model lifecycle automation and strong governance.

Qualifications

• Hands-on experience deploying ML models to production.

Responsibilities

• 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.

Skills

Python

APIs

ML deployment

CI/CD

ML lifecycle

Mentoring

Communication

Tools

Databricks

MLflow

Airflow

Kubeflow

SageMaker

Vertex AI

Terraform

CloudFormation

LangChain

LangGraph

Job description

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.

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.

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.