Senior Python / AI Engineer — Core Team @ Zumlo
Zumlobaddi, India8 hours ago
Job descriptionPrivacy & trust Evidence over hype Inclusive by default 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. Do RAG right : chunking / segmenters, embeddings, vector DBs (FAISS / qdrant / Pinecone / Milvus), hybrid semantic + re-rankers (BGE / ColBERT), dedupe, freshness policies, provenance. 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. PHI / PII redaction, prompt-injection defenses, output guardrails, rate limits, audit trails, safe logging. Clear data-handling notes for the team. 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. Trustworthy event tracking, simple SQL cohorting, per-feature cost / latency dashboards, A / B hooks so product / growth can run honest tests. Evaluate models / embeddings, inference servers (vLLM / TensorRT-LLM), compression / quantization, token-efficiency. Prove value with small, cheap spikes before big changes. 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. 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. 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. 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. 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.
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
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)
Retrieval & knowledge
Rapid test & evaluation loop
Safety, privacy, governance
Backend & reliability
Data & experimentation
Exploration & de-risking
Collaboration & leadership
Must-have experience
Strong plus
How you work (what we value)
Work setup
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Senior Engineer Python • baddi, India
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