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Full-Stack Data Scientist AI/ML

Ntrinsic Consulting
Bath
3 weeks ago
Applications closed

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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:
  • Deployment of ML Models or applications using DevOps pipelines.
  • Managing the entire ML lifecycle—from training and validation to versioning, deployment, monitoring, and governance.
  • Post-model deployment MLOps experience.
  • Building automation pipelines and CI/CD integrations for ML projects using tools such as Jenkins and Maven.
  • Solving common challenges faced by Data Scientists, including model reproducibility, deployment portability, and environment standardization.
National AI Awards 2025

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