- Please note: This is a 100% onsite position located in Bangalore, India. Kindly apply only if you are willing and able to work on-site from our Bangalore office.
- Office address (Bangalore) : HM Eleganza, 31 & 31/1, Museum Rd, near Church Street, Shanthala Nagar, Ashok Nagar, Bengaluru, Karnataka 560001
- Own the multi‑track plan for ML projects (scoping → delivery), including timelines, dependencies, resources, and risk/RAID management.
- Run the operating rhythm: backlog/roadmap, sprint planning, stand‑ups, demos, and executive readouts with crisp status and decision logs.
- Define success criteria and measurable outcomes (quality, latency, cost, safety), then track and improve them.
- Align Product, Engineering, Clinical, and Customer teams around priorities; drive decisions and unblock fast.
- Translate ambiguous problem statements into clear problem definitions, milestones, and acceptance criteria.
- Coordinate data pipelines, annotations, experimentation, and evaluation—shipping production‑ready ML with reliability.
- Review designs/PRDs, sanity‑check experiments, and dive into notebooks or dashboards to resolve issues when needed.
- Partner on MLOps best practices (versioning, CI/CD for models, observability, guardrails, rollback plans).
- Establish evaluation frameworks for LLM/RAG and clinical NLP (offline metrics, red‑teaming, human‑in‑the‑loop QA).
- Mentor ML engineers/data scientists; set clear expectations, feedback loops, and growth paths.
- Improve the system—templates, playbooks, runbooks, postmortems—to compound team impact as we scale.
- 10+ years in Applied ML/DS/AI (or ML‑heavy product/engineering), including 5+ years leading multi‑workstream ML programs or teams.
- Proven track record shipping ML/LLM systems to production with clear business outcomes.
- Strong program management fundamentals (road‑mapping, risk management, stakeholder alignment) and excellent written/verbal communication.
- Working knowledge of modern ML/LLM tooling (Python, PyTorch/TensorFlow, experiment tracking, data/feature stores, eval frameworks, model observability).
- Ability to operate in the final mile—closing loops with high judgment, urgency, and attention to detail.
- Healthcare curiosity and comfort with privacy, safety, and compliance considerations.
- Experience with RAG pipelines, clinical NLP (e.g., de‑identification, coding, entity linking), or payer/provider workflows.
- Background building MLOps platforms or evaluation harnesses for LLMs.
- Experience mentoring/hiring ML talent and leading vendors/partners.
Skills Required
- 10+ years of experience in applied machine learning, data science, AI, or ML-heavy product or engineering roles
- 5+ years leading multi-workstream machine learning programs or teams
- Proven experience shipping machine learning or LLM systems to production with measurable business outcomes
- Strong program management skills, including roadmapping, risk management, and stakeholder alignment
- Excellent written and verbal communication skills
- Working knowledge of Python, PyTorch or TensorFlow, experiment tracking, data and feature stores, evaluation frameworks, and model observability
- Ability to close execution loops with high judgment, urgency, and attention to detail
- Healthcare curiosity and comfort with privacy, safety, and compliance considerations
- Experience with RAG pipelines, clinical NLP, de-identification, coding, entity linking, or payer/provider workflows
- Experience building MLOps platforms or LLM evaluation harnesses
- Experience mentoring or hiring machine learning talent
- Experience leading vendors or partners
What We Do
Autonomize AI Agents & Copilots organize, contextualize and summarize unstructured data to reduce the administrative burden for healthcare knowledge workers to make data-driven decisions and improve patient outcomes. Our customers include health plans, providers and life sciences companies. Unlike generic AI systems retrofitted for healthcare, Autonomize deeply understands medical contexts, terminologies, and operational nuances. Our healthcare-focused AI Agents & Copilots augment knowledge work, drastically reducing administrative burden. Care management teams spend 78% less time per case, achieving an impressive 85% boost in case review efficiency. Prior authorization processes that traditionally take 20-30 minutes shrink to mere seconds, accompanied by an 80% reduction in manual errors, saving millions of dollars annually. Our AI Agents turn chaotic, unstructured healthcare data—clinical notes, PDFs, faxes, and claims—into structured, contextual information that informs decisions and actions. This has driven substantial real-world impact: organizations using Autonomize experience a 92% reduction in manual effort for care gaps and HEDIS chart reviews, dramatically improving compliance and STAR ratings. Autonomize AI is purpose-built for healthcare, transforming healthcare operations one workflow at a time through AI-native solutions that deliver immediate, scalable impact.
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