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Cerebry — GenAI Implementation Engineer (AI Growth Lead)

Cerebry — GenAI Implementation Engineer (AI Growth Lead)

Cerebryrajkot, gujarat, in
7 days ago
Job description

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

  • Focused 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)

    • 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 : info@cerebry.co

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    Implementation Engineer • rajkot, gujarat, in