Cerebry - GenAI Implementation Engineer (AI Growth Lead)

Mission Transform Cerebry Research designs into production-grade GenAI features-retrieval-grounded, safe, observable, and ready for seamless product rollout. Architect, code, evaluate, and package GenAI services that power Cerebry end-to-end. Why this is exciting (Ownership-Forward) - Founder-mindset equity. We emphasize meaningful ownership from day one. - Upside compounds with impact. Initial grants are designed for real participation in value creation, with refresh opportunities tied to scope and milestones. - Transparent offers. We share the full comp picture (salary, equity targets, vesting cadence, strike/valuation context) during the process. - Long-term alignment. Packages are crafted for builders who want to grow the platform and their stake as it scales. What you'll build - Retrieval & data grounding: connectors for warehouses/blobs/APIs; schema validation and PII-aware pipelines; chunking/embeddings; hybrid search with rerankers; multi-tenant index management. - Orchestration & reasoning: function/tool calling with structured outputs; controller logic for agent workflows; context/prompt management with citations and provenance. - Evaluation & observability: gold sets + LLM-as-judge; regression suites in CI; dataset/version tracking; traces with token/latency/cost attribution. - Safety & governance: input/output filtering, policy tests, prompt hardening, auditable decisions. - Performance & efficiency: streaming, caching, prompt compression, batching; adaptive routing across models/providers; fallback and circuit strategies. - Product-ready packaging: versioned APIs/SDKs/CLIs, Helm/Terraform, config schemas, feature flags, progressive delivery playbooks. Outcomes you'll drive - Quality: higher factuality, task success, and user trust across domains. - Speed: rapid time-to-value via templates, IaC, and repeatable rollout paths. - Unit economics: measurable gains in latency and token efficiency at scale. - Reliability: clear SLOs, rich telemetry, and smooth, regression-free releases. - Reusability: template repos, connectors, and platform components adopted across product teams. How you'll work - Collaborate asynchronously with Research, Product, and Infra/SRE. - Share designs via concise docs and PRs; ship behind flags; measure, iterate, and document. - Enable product teams through well-factored packages, SDKs, and runbooks. Tech you'll use - LLMs & providers: OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock; targeted OSS where it fits. - Orchestration/evals: LangChain/LlamaIndex or lightweight custom layers; test/eval harnesses. - Retrieval: pgvector/FAISS/Pinecone/Weaviate; hybrid search + rerankers. - Services & data: Python (primary), TypeScript; FastAPI/Flask/Express; Postgres/BigQuery; Redis; queues. - Ops: Docker, CI/CD, Terraform/CDK, metrics/logs/traces; deep experience in at least one of AWS/Azure/GCP. What you bring - A track record of shipping and operating GenAI/ML-backed applications in production. - Strong Python, solid SQL, and systems design skills (concurrency, caching, queues, backpressure). - Hands-on RAG experience (indexing quality, retrieval/reranking) and function/tool use patterns. - Experience designing eval pipelines and using telemetry to guide improvements. - Clear, concise technical writing (design docs, runbooks, PRs). Success metrics - Evaluation scores (task success, factuality) trending upward - Latency and token-cost improvements per feature - SLO attainment and incident trends - Adoption of templates/connectors/IaC across product teams - Clarity and usage of documentation and recorded walkthroughs Hiring process 1. Focused coding exercise (2-3h): ingestion retrieval tool-calling endpoint with tests, traces, and evals 2. Systems design (60m): multi-tenant GenAI service, reliability, and rollout strategy 3. GenAI deep dive (45m): RAG, guardrails, eval design, and cost/latency tradeoffs 4. Docs review (30m): discuss a short design doc or runbook you've written (or from the exercise) 5. Founder conversation (30m) Apply Share links to code (GitHub/PRs/gists) or architecture docs you authored, plus a brief note on a GenAI system you built-problem, approach, metrics, and improvements over time. Email: