Gen AI/ML Data Scientist

Capgemini•Seattle, WA

22h•Hybrid

About The Position

This role focuses on Generative AI, intelligent applications, and advanced analytics, combining hands-on AI engineering with a delivery and consulting-oriented mindset. You will work closely with business and technical stakeholders to shape, build, and scale AI solutions from early exploration through production delivery. Some engagements may follow a Forward Deployment Engineer (FDE)-style working model, where engineers collaborate closely with client teams during solution design and rollout. The role is broad and well suited for candidates who enjoy combining strong technical problem-solving with collaborative, client-facing delivery. Many initiatives are centered on enterprise cloud and AI platforms, with a strong preference for Azure-based architectures and services, as well as modern developer environments that incorporate AI-assisted development and code generation tools.

Requirements

Bachelor's degree or higher in Computer Science, AI/ML, Data Science, Engineering, or equivalent experience.
1+ years of experience in AI/ML, GenAI, application development, or data engineering.
Strong proficiency in Python and SQL.
Experience with cloud platforms (Azure strongly preferred; AWS or GCP also considered).
Experience with APIs, CI/CD pipelines, containerization (Docker), and cloud native deployments.
Prior experience in enterprise, consulting, or client delivery environments preferred.

Nice To Haves

Experience delivering solutions on Azure or Microsoft aligned platforms, including AI, data, or application services.
Familiarity with Microsoft native developer tools and AI assisted coding / code generation tools.
Exposure to agentic or tool orchestrated AI patterns.
Interest or experience in client proximate or forward deployed engineering roles (a plus, not required).

Responsibilities

Design, develop, and deploy AI enabled applications aligned to enterprise needs.
Translate business problems into scalable technical solutions.
Build Generative AI solutions using large language models, including RAG and tool enabled workflows.
Apply AI assisted code generation and developer productivity tools for tasks such as code scaffolding, refactoring, documentation, and test generation.
Design and integrate application components using REST APIs, authentication, error handling, and observability best practices.
Deploy and operate AI solutions in production or production adjacent environments, supporting monitoring and reliability.
In some cases, work in a client proximate or forward deployed delivery model in close collaboration with architects and delivery leads.