Technology Scouting & Evaluation
Run short, instrumented spikes and benchmarking on new models, tools and frameworks: LLMs, agentic systems, vector databases, orchestration frameworks, copilots, assistants and more.
Compare vendor and open-source options, documenting trade-offs across quality, cost, latency, security and integration complexity.
Deliver concise decision memos with clear recommendations: adopt, watch, or avoid.
Evaluation Harnesses & Sandboxes
Design and maintain evaluation environments (e.g. datasets, prompts, scenarios, telemetry) to test models under realistic constraints.
Build automation and tooling to measure quality, robustness, latency and cost, including regression tracking over time.
Ensure every evaluated technology has benchmark coverage and a documented risk and limitations view.
Prototyping & Technical Validation
Build enough of a system to understand how a technology behaves under realistic conditions, not just in vendor demos or isolated examples.
Explore architecture, integration patterns, operational constraints, security boundaries, and failure modes through working prototypes.
Determine what must be true for a proof of concept to become a viable production capability.
Prefer focused prototypes that answer specific technical questions over prematurely building production systems.
Recommendations, Readiness & Hand-offs
Translate technical findings into clear decision memos for both technical and non-technical stakeholders.
For validated technologies, produce readiness guidance covering recommended patterns, guardrails, known limitations, operational considerations, and integration requirements.
Coordinate hand-offs to the engineering teams responsible for productionization.
Support those teams during the transition when deep context from the evaluation is required, without becoming the permanent owner of the resulting system.
Track what happens after Lab recommendations and use those outcomes to improve future evaluation methods.
Governance, Risk & Standards
Work with Security, Legal, Compliance and other AI teams to document risk assessments, mitigations and governance recommendations for each evaluated technology.
Maintain checklists, decision templates and lightweight standards reusable across evaluations and by partner teams.
Incorporate learnings from third-party AI tooling already in use, such as external copilots and the AWS AI suite, into adoption guidelines.
Collaboration, Mentoring & Community
Partner with other AI teams and domain teams to ensure clear boundaries and smooth collaboration.
Participate in hiring as a technical evaluator and culture champion.
Mentor engineers in the Lab and adjacent teams on evaluation methods, benchmarking and experimental design.
Share knowledge through internal write-ups, tech talks and occasional external meetups and conferences.