Cerebry — GenAI Implementation Engineer (AI Growth Lead)

0 years

0 Lacs

Posted:2 days ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

Job Description

Mission


production-grade GenAI features


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


code


info@cerebry.co

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