ML Engineer (with Data Science background)

ML Ops engineer with Data Science background (AWS services) - London/remote - £536 per day

ML Ops engineer with experience in data science, DevOps, and AWS SageMaker, along with a solid understanding of Agile software development principles. In this role, you will act as a bridge between Data Scientists and IT DevOps Engineers, helping translate experimental ML models into scalable, production-ready applications. You'll play a critical role in building practical solutions to real-world data science challenges, including automating workflows, packaging models, and deploying them as microservices using AWS services.

The ideal candidate will be adept at developing end-to-end applications to serve AI/ML models, including those from platforms like Hugging Face, and will work with a modern AWS-based toolchain (SageMaker, Fargate, Bedrock).

Your core responsibilities include:

• Serve as the day-to-day liaison between Data Science and DevOps, ensuring effective deployment and integration of AI/ML solutions using AWS services.
• Assist DevOps engineers with packaging and deploying ML models, helping them understand AI specific requirements and performance nuances.
• Design, develop, and deploy standalone and micro-applications to serve AI/ML models, including Hugging Face Transformers and other pre-trained architectures.
• Build, train, and evaluate ML models using services such as AWS SageMaker, Bedrock, Glue, Athena, Redshift, and RDS.
• Help create the knowledge artefacts for Data Scientist around DevOps and ML Ops.
• Where required, hand hold the data scientist and assist them with DevOps engineering issues, package installation issues, creating a Docker container, ML Ops tooling issues.
• Develop and expose secure APIs using Apigee, enabling easy access to AI functionality across the organization.
• Manage the entire ML life cycle-from training and validation to versioning, deployment, monitoring, and governance.
• Build automation pipelines and CI/CD integrations for ML projects using tools like Jenkins and Maven.
• Solve common challenges faced by Data Scientists, such as model reproducibility, deployment portability, and environment standardization.
• Assist the product owner to define and implement the ML Ops roadmap.
• Support knowledge sharing and mentorship across data Scientists teams, promoting a best practice-first culture.

What skills are required?

Minimum skills:

• Degree in computer science, economics, data science or another technical field (eg maths, physics, statistics etc.), or equivalent relevant experience
• Strong programming proficiency in Python (or R), with practical experience in machine learning and statistical modelling.
• Proven experience delivering end-to-end data science products, including both experimentation and deployment.
• Solid understanding of data cleaning, feature engineering, and model performance evaluation.

Essential skills:

• Demonstrated experience deploying and maintaining AI/ML models in production environments.
• Hands-on experience with AWS Machine Learning and Data services: SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, and Redshift.
• Familiarity with deploying Hugging Face models (eg, NLP, vision, and generative models) within AWS environments.
• Ability to develop and host microservices and REST APIs using Flask, FastAPI, or equivalent frameworks.
• Proficiency with SQL, version control (Git), and working with Jupyter or RStudio environments.
• Experience integrating with CI/CD pipelines and infrastructure tools like Jenkins, Maven, and Chef.
• Strong cross-functional collaboration skills and the ability to explain technical concepts to non technical stakeholders.
• Ability to work across cloud-based architectures.

Tools & Technologies:

• AWS Services: SageMaker, Bedrock, Glue, ECS Fargate, Athena, Kendra, RDS, Redshift, Lambda, CloudWatch
• Other Tooling: Apigee, Hugging Face, RStudio, Jupyter, Git, Jenkins, Linux
• Languages & Frameworks: Python, R, Flask, FastAPI, SQL