GenAI Developer-JN 79

THE OVERALL PURPOSE OF POSITION:

Support AI pod and analyze client needs stand up prototypes and convert those prototypes into secure production-ready applications.

Will own the end-to-end development life cycle for changing client efforts while constantly scouting testing and injecting emerging AI technologies into the process.

Candidate must pair an exceptional delivery discipline with hands-on engineering agility moving seamlessly from whiteboard to working demo and back again.

SCOPE OF WORK:

Exploration & Scoping

Rapidly synthesize client pain points into concise problem statements success metrics and guardrails

Evaluate feasibility cost risk and ROI for multiple GenAI solution paths (RAG agentic workflows synthetic-data pipelines etc.)

Aid in maintaining a dynamic backlog of ideas continuously assisting in reprioritized against business value

Rapid Build & Iteration

Spin up end-to-end prototypes using technologies like AWS Bedrock / SageMaker FastAPI Streamlit React or whatever framework or architecture efficiently to meet client needs

Implement evaluation harnesses for accuracy latency and cost; demo tangible progress each Sprint.

Capture stakeholder feedback live and fold it into the next sprint with minimal overhead

Adapt & Enhance

Refactor architecture as user requirements shift or new data sources emerge

Introduce cutting-edge libraries or architectures when they offer measurable uplift

Document decision trade-offs and lessons learned for future waves

Hardening & Transition

Elevate promising POCs into client-compliant pilots and integrate CI/CD pipelines

Produce all delivery artifacts: architecture diagrams security traceability runbooks video demos

Alignment to USAC and AFS security practices

Creating/Updating existing relevant client documentation

o Aiding in creation of video demos

o Conducting live demos

Guide pilots through acceptance hand-off and sustainment planning with operations teams

Emerging-Tech Tracking & Best Practices

Track novel GenAI trends (open-source LLMs small-context agents memoryefficient RAG) and run spike proofs to gauge fit

Present briefings to the Innovation leadership (when applicable) and seed reusable patterns into our internal asset library

Mentor junior practitioners on prompt engineering token-cost governance and evaluation best practices

QUALIFICATION AND EXPERINECE:

Bachelors degree in technology innovation or related field of study

3 years delivering data products or full stack applications

1 years building & deploying GenAI applications

Practical understanding of technologies like:

o AWS core services using Bedrock or SageMaker for LLM hosting

o Java Python FastAPI Docker GitHub Actions Terraform or AWS CDK

o React / TypeScript and modern vector stores

o LangChain LlamaIndex LangGraph or equivalent RAG / agent frameworks

Demonstrated ability to compress ideation demo hardened pilot into < 90 days

Exceptional verbal written and presentation skills

Able to translate technical insights into executive-ready stories

High comfort operating in fast-paced ambiguous environments where priorities shift rapidly