Full-Stack Data Scientist AI/ML

Full-Stack Data Scientist AI/ML Location: Hybrid – 40% on-site (client site, UK) Security Clearance: Active SC or SC Eligible – Mandatory Start Date: Immediate Rate: negotiable with experience 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. 1. Managing the entire ML lifecycle—from training and validation to versioning, deployment, monitoring, and governance. 1. Post-model deployment MLOps experience. 1. Building automation pipelines and CI/CD integrations for ML projects using tools such as Jenkins and Maven. 1. Solving common challenges faced by Data Scientists, including model reproducibility, deployment portability, and environment standardization.