Senior Python / AI Engineer — Core Team @ Zumlo

About Zumlo Zumlo 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 default The 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 own Product 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 & knowledge Do RAG right: chunking/segmenters, embeddings, vector DBs (FAISS/qdrant/Pinecone/Milvus), hybrid semantic + re-rankers (BGE/ColBERT), dedupe, freshness policies, provenance. Rapid test & evaluation loop Make 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, governance PHI/PII redaction, prompt-injection defenses, output guardrails, rate limits, audit trails, safe logging. Clear data-handling notes for the team. Backend & reliability Own 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 & experimentation Trustworthy event tracking, simple SQL cohorting, per-feature cost/latency dashboards, A/B hooks so product/growth can run honest tests. Exploration & de-risking Evaluate models/embeddings, inference servers (vLLM/TensorRT-LLM), compression/quantization, token-efficiency. Prove value with small, cheap spikes before big changes. Collaboration & leadership Partner 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 experience 7+ 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 plus Well-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 setup Remote-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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