Location: Hybrid – 40% on-site (client site, UK)
Security Clearance: Active SC or SC Eligible – Mandatory
Start Date: Immediate

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. 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.

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.
• 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.
• Develop and expose secure APIs using Apigee, enabling easy access to AI functionality across the
• Manage the entire ML lifecycle—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
• Solve common challenges faced by Data Scientists, such as model reproducibility, deployment portability, and environment standardization.
• Support knowledge sharing and mentorship across data Scientists teams, promoting a best- practice-first culture.

Essential skills:

• Demonstrated experience deploying and maintaining AI/ML models in production
• 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 (e.g., NLP, vision, and generative models) within AWS environments.
• Ability to develop and host microservices and REST APIs using Flask, FastAPI, or equivalent
• Proficiency with SQL, version control (Git), and working with Jupyter or RStudio
• Experience integrating with CI/CD pipelines and infrastructure tools like Jenkins, Maven, and
• Strong cross-functional collaboration skills and the ability to explain technical concepts to non- technical stakeholders.
• Ability to work across cloud-based

working experience in the following areas: 

1. Deployment of ML Models or applications using DevOps pipelines.
2. Managing the entire ML lifecycle—from training and validation to versioning, deployment, monitoring, and governance.
3. Post-model deployment MLOps experience.
4. Building automation pipelines and CI/CD integrations for ML projects using tools such as Jenkins and Maven.
5. Solving common challenges faced by Data Scientists, including model reproducibility, deployment portability, and environment standardization.