AI Developer

“Mindlance•Washington, DC

10d•Hybrid

About The Position

Support “AI pod” and analyse 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.

Requirements

Bachelor's 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: AWS core services using Bedrock or SageMaker for LLM hosting
Java, Python, FastAPI, Docker, GitHub Actions, Terraform or AWS CDK
React / Next.js, TypeScript, and modern vector stores
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.

Responsibilities

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.
Spin up end-to-end prototypes using technologies like AWS Bedrock / SageMaker, FastAPI, Streamlit React, Next.js, 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.
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.
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 client and AFS security practices.
Creating/Updating existing relevant client documentation.
Aiding in creation of video demos
Conducting live demos
Guide pilots through acceptance, hand-off, and sustainment planning with operations teams.
Track Client GenAI trends (open-source LLMs, small-context agents, memory-efficient 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.