Cerebry — GenAI Implementation Engineer (AI Growth Lead)

MissionTransform Cerebry Research designs intoproduction-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 emphasizemeaningful ownershipfrom day one. Upside compounds with impact.Initial grants are designed for real participation in value creation, withrefresh opportunitiestied 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 togrow the platform and their stakeas it scales.What you’ll buildRetrieval & data grounding:connectors for warehouses/blobs/APIs; schema validation and PII-aware pipelines; chunking/embeddings;hybrid searchwith 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 driveQuality: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 workCollaborateasynchronouslywith 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 useLLMs & 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 bringA track record ofshipping and operating GenAI/ML-backed applicationsin production. StrongPython , solidSQL , and systems design skills (concurrency, caching, queues, backpressure). Hands-onRAGexperience (indexing quality, retrieval/reranking) andfunction/tool usepatterns. Experience designingeval pipelinesand using telemetry to guide improvements. Clear, concise technical writing (design docs, runbooks, PRs).Success metricsEvaluation 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 walkthroughsHiring processFocused coding exercise (2–3h):ingestion → retrieval → tool-calling endpoint with tests, traces, and evals Systems design (60m):multi-tenant GenAI service, reliability, and rollout strategy GenAI 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)ApplyShare links tocode(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:

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