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.