Cerebry — genai implementation engineer

Mission Transform Cerebry Research designs into production-grade Gen AI features —retrieval-grounded, safe, observable, and ready for seamless product rollout. Architect, code, evaluate, and package Gen AI 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, Ia C, 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: Open AI, Anthropic, Google, Azure Open AI, AWS Bedrock; targeted OSS where it fits. Orchestration/evals: Lang Chain/Llama Index or lightweight custom layers; test/eval harnesses. Retrieval: pgvector/FAISS/Pinecone/Weaviate; hybrid search + rerankers. Services & data: Python (primary), Type Script; Fast API/Flask/Express; Postgres/Big Query; 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 Gen AI/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/Ia C across product teams Clarity and usage of documentation and recorded walkthroughs Hiring process Focused coding exercise (2–3h): ingestion → retrieval → tool-calling endpoint with tests, traces, and evals Systems design (60m): multi-tenant Gen AI service, reliability, and rollout strategy Gen AI deep dive (45m): RAG, guardrails, eval design, and cost/latency tradeoffs Docs review (30m): discuss a short design doc or runbook you’ve written (or from the exercise) Founder conversation (30m) Apply Share links to code (Git Hub/PRs/gists) or architecture docs you authored, plus a brief note on a Gen AI system you built—problem, approach, metrics, and improvements over time. Email: