Full-Stack Data Scientist AI/ML

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Principal Consultant - 360 Recruiter | Account Management | Business Development

Full-Stack Data Scientist AI/ML

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.
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.

Seniority level

Seniority level
Not Applicable

Employment type

Employment type
Contract

Job function

Job function
Information Technology
Industries
IT Services and IT Consulting

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