Senior Python / AI Engineer — Core Team @ Zumlo

About ZumloZumlo is an always-on well-being companion—one place for immediate help, gentle structure, and progress you can see. We unify mind, body, emotions, and relationships through timely support, a caring community, and personalized guidance that fits real life.What we stand for: Human first • Simple > complex • Privacy & trust • Evidence over hype • Inclusive by defaultThe role (why it’s rare)AI isn’t a bolt-on here—it’s the nervous system of the product. We’re hiring a senior who has already shipped LLM + retrieval to production and wants end-to-end ownership: problem framing, modeling/orchestration, evaluation, privacy/safety, and the Python services that make it real.What you will ownProduct AI (end-to-end)Build AI across product surfaces: conversational help, guided steps, tailored activities, “what to do next,” summaries/explanations, and safety checks—grounded with retrieval and citations.Turn fuzzy needs into robust flows: prompt design, tool/function calling, JSON-schema outputs, fallbacks, streaming, controllable tone and safety boundaries.Retrieval & knowledgeDo RAG right: chunking/segmenters, embeddings, vector DBs (FAISS/qdrant/Pinecone/Milvus), hybrid semantic + re-rankers (BGE/ColBERT), dedupe, freshness policies, provenance.Rapid test & evaluation loopMake eval routine: golden sets, adversarial suites, shadow evals, canaries, online metrics tied to user outcomes. Capture in-app feedback and close the loop weekly.Safety, privacy, governancePHI/PII redaction, prompt-injection defenses, output guardrails, rate limits, audit trails, safe logging. Clear data-handling notes for the team.Backend & reliabilityOwn Python services: FastAPI, Postgres (schemas/migrations), Redis, task queues, retries/idempotency, auth/RBAC, feature flags.Observability first: logs/metrics/traces, alerting that matters, simple SLOs—systems that are predictable and calm.Data & experimentationTrustworthy event tracking, simple SQL cohorting, per-feature cost/latency dashboards, A/B hooks so product/growth can run honest tests.Exploration & de-riskingEvaluate models/embeddings, inference servers (vLLM/TensorRT-LLM), compression/quantization, token-efficiency. Prove value with small, cheap spikes before big changes.Collaboration & leadershipPartner with product, mobile (React Native/TS), and platform.Review PRs, write concise docs/runbooks, mentor juniors, and help hire the next 1–2 great engineers.Must-have experience7+ years software engineering with 4+ years Python shipping production APIs/services.Production LLM + RAG you can discuss end-to-end: retrieval, orchestration, evaluation, and user impact.RAG depth: embeddings, vector DBs (FAISS/qdrant/Pinecone/Milvus), hybrid search, re-rankers, citation strategies.Backend foundation: FastAPI/Django/Flask, Postgres/SQL, Redis, queues (Celery/RQ), testing (pytest), CI/CD, containers.Eval mindset: offline metrics + online behavior; sample-size sense; knows when to ship, iterate, or kill.Security/privacy: least-privilege, secrets, encryption, safe logging; comfortable with sensitive data.Strong plusWell-being/health context; HIPAA-aware practices.Azure & Azure DevOps pipelines; GPU inference; streaming responses.Telemetry for AI: prompt/version tracking, per-feature cost/latency, drift monitors.Worked across US + India user bases/time zones.How you work (what we value)Builder energy: ship → measure → iterate.Creative + logical: playful with ideas, strict with evidence.Product-curious: start with the user problem and “definition of good.”Kind, direct, low-ego: crisp commits/PRs, generous feedback.Owner’s mindset: reliable, documented, observable.Work setupRemote-first in India, collaborating closely with a small core team.Preference for Ahmedabad for an eventual in-person cadence; open across India for the right fit.

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