AI Innovation Engineer

Steampunk•McLean, VA

About The Position

We are seeking a hands-on, creative, and technically strong AI Innovation Engineer to support our client delivery teams in bringing modern AI solutions to life within public-sector organizations. This role is focused on client enablement, prototyping, and cross-functional engagement—helping our clients understand the potential of AI, design proofs-of-concept, and build working prototypes that demonstrate value in their specific mission context. The ideal candidate combines strong technical skills (especially with GenAI, LLMs, data pipelines, and cloud-native services) with the ability to collaborate across disciplines—working with DevSecOps teams, cloud working groups, data stewards, and communities of practice to drive AI adoption and build momentum. You’ll play a critical role in bridging the gap between strategy and execution, demonstrating how AI can solve real-world problems and catalyze digital transformation. Contributions This role is ideal for someone who thrives at the intersection of technical innovation and human-centered impact. You’ll help our clients not only understand what AI can do, but also show them how through prototypes, pilots, and practice. You’ll be an early advocate for the latest AI service offerings that are available for our clients. You'll join a collaborative, forward-leaning culture that values exploration, rapid iteration, and practical impact. You’ll contribute to the growth of our AI & Data Exploitation Practice.

Requirements

Ability to hold a position of public trust with the US government.
Bachelor’s or Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
3+ years of experience in software engineering, data engineering, or applied AI/ML roles.
Demonstrated hands-on experience with modern GenAI tooling, such as:
OpenAI (GPT-4), Claude, Gemini, Llama 3
LangChain, LlamaIndex, or similar RAG frameworks
AWS Bedrock, GCP Vertex AI, Azure OpenAI Service
Vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma)
Strong Python development skills with familiarity in building data pipelines, API wrappers, and lightweight front-ends for demos.
Ability to build and present AI-powered demos to both 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 NIST AI Risk Management Framework and other public-sector digital policy memos.
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 standing up or contributing to an AI Community of Practice or Innovation Hub.
Relevant Professional Certification(s)
Experience supporting HHS or NIH programs
Experience working in scientific environments

Responsibilities

Serve as a trusted advisor to the client organization on modern AI capabilities, best practices, and responsible use
Develop AI education materials, tutorials, demo videos, and onboarding sessions that are tailored to the client organization’s needs
Lead or support AI enablement events such as workshops, lunch-and-learns, innovation sprints, and capability briefings
Design and implement AI prototypes and proof-of-concept solutions tailored to specific mission use cases (e.g., summarization, entity extraction, classification, semantic search)
Work with cloud platform teams to integrate prototypes into modern cloud environments (e.g., AWS Bedrock, GCP Vertex AI, Azure OpenAI)
Leverage RAG pipelines, orchestration frameworks (LangChain, LlamaIndex), and LLM APIs to rapidly deliver working demos
Participate in communities of practice and cross-team working groups (e.g., cloud, data, architecture, cybersecurity) to ensure AI adoption is aligned with broader modernization goals
Share findings, prototype patterns, and reusable components across delivery teams to accelerate reuse and scale impact
Serve as a liaison between data scientists, platform engineers, and user-facing teams to ensure end-to-end viability of AI solutions
Ensure all AI solutions are designed with responsible AI principles, data sensitivity awareness, and security-first practices
Collaborate with compliance and governance stakeholders to vet tools, define usage boundaries, and mitigate risks in AI application