GenAI Platform Architect

3 weeks ago


Industrial Area, India Cerebry Full time
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 acrossmodels/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: info@cerebry.co



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