AI Innovation Lead

Steampunk•McLean, VA

2d•$145,000 - $185,000

About The Position

Steampunk is seeking a hands-on, creative, and technically strong AI Innovation Lead to support client delivery teams in bringing modern AI solutions to life within public-sector organizations. The role focuses on client enablement, prototyping, and cross-functional engagement. The AI Innovation Lead helps clients understand AI capabilities, design proofs of concept, and build working prototypes that demonstrate value in their mission contexts. The role combines technical skills in generative AI, large language models, data pipelines, and cloud-native services with collaboration across DevSecOps teams, cloud working groups, data stewards, and communities of practice. It bridges strategy and execution by demonstrating how AI can address practical problems and support digital transformation. The position contributes to the growth of Steampunk’s AI & Data Exploitation Practice and involves advocating for available AI service offerings. The position also requires designing solutions around responsible AI principles, data sensitivity, and security-first practices, and collaborating with compliance and governance stakeholders to assess tools, define usage boundaries, and mitigate risks. The projected annual compensation range is $145,000 to $185,000.

Requirements

Ability to hold a position of public trust with the US government.
Bachelor’s degree and 10 or more years of work experience, or an equivalent combination of education and experience.
At least 5 years of experience in software engineering, data engineering, or applied AI/ML roles.
Hands-on experience with modern generative AI tools such as GPT-4, Claude, Gemini, or Llama 3.
Experience with LangChain, LlamaIndex, or similar RAG frameworks.
Experience with AWS Bedrock, GCP Vertex AI, or Azure OpenAI Service.
Experience with vector databases such as FAISS, Pinecone, Weaviate, or Chroma.
Strong Python development skills and familiarity with building data pipelines, API wrappers, and lightweight front ends for demonstrations.
Ability to build and present AI-powered demonstrations to technical and non-technical audiences.
Familiarity with DevSecOps and CI/CD principles.
Familiarity with GitHub.
Experience engaging with external clients or business stakeholders in a consultative role.

Nice To Haves

Experience delivering AI capabilities into federal government agencies or highly regulated industries.
Experience customizing AI services for biomedical or health research.
Understanding of the NIST AI Risk Management Framework and other public-sector digital policy memoranda.
Hands-on knowledge of data governance, PII handling, and compliance-aware prototyping.
Exposure to Human-Centered Design practices, user research synthesis, or journey mapping.
Prior involvement in establishing or contributing to an AI Community of Practice or Innovation Hub.

Responsibilities

Advise client organizations on modern AI capabilities, best practices, and responsible use.
Develop AI education materials, tutorials, demo videos, and onboarding sessions tailored to client needs.
Lead or support AI enablement events, including workshops, lunch-and-learns, innovation sprints, and capability briefings.
Design and implement AI prototypes and proofs of concept tailored to mission use cases such as summarization, entity extraction, classification, and semantic search.
Work with cloud platform teams to integrate prototypes into cloud environments such as AWS Bedrock, GCP Vertex AI, and Azure OpenAI.
Use RAG pipelines, orchestration frameworks, and LLM APIs to deliver working demonstrations.
Participate in communities of practice and cross-team working groups to align AI adoption with broader modernization goals.
Share findings, prototype patterns, and reusable components across delivery teams.
Liaise between data scientists, platform engineers, and user-facing teams to support end-to-end viability of AI solutions.
Apply responsible AI principles, data sensitivity awareness, and security-first practices to AI solutions.
Collaborate with compliance and governance stakeholders to vet tools, define usage boundaries, and mitigate risks in AI applications.
Build and present AI-powered demonstrations to technical and non-technical audiences.