Lead and program-manage a portfolio of ML initiatives at a fast-growing healthcare AI startup, driving LLM/RAG, clinical NLP, and MLOps projects from ambiguity to production.

About the Company

Autonomize AI is a fast-growing healthcare technology startup based in Bangalore, India. The company is dedicated to revolutionizing healthcare by streamlining knowledge workflows with artificial intelligence. By reducing administrative burdens and elevating clinical outcomes, Autonomize AI empowers healthcare professionals to focus on what truly matters — improving patient lives. The team operates with a strong sense of urgency, collaboration, and technical excellence.

Role Overview

We are hiring a Manager, Machine Learning Engineer - Applied AI to own the program management and technical leadership of a portfolio of ML initiatives. This is a 100% onsite position at our Bangalore office (HM Eleganza, near Church Street). The role is perfect for a seasoned technical leader who thrives in ambiguity, can align cross-functional teams, and drive multiple ML tracks — from LLM/RAG workflows to clinical NLP and MLOps — all the way to production. You will not only set direction but also dive deep into the work, ensuring high-quality execution and measurable outcomes.

Key Responsibilities

• Program & Portfolio Leadership

  • Own multi-track plans for ML projects including scoping, delivery, 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) and track them continuously.

• Cross-Functional Execution

  • 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.

• Technical Depth & MLOps

  • 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).

• People & Scale

  • 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.

Tech Stack

• Python, PyTorch, TensorFlow
• LLM frameworks (RAG pipelines, LangChain, LlamaIndex)
• Experiment tracking (MLflow, Weights & Biases)
• Data/feature stores (Feast, etc.)
• Model observability and evaluation harnesses
• MLOps tooling (CI/CD for models, versioning, monitoring)
• Clinical NLP libraries and de-identification tools

Must-Have Qualifications

• 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.

What Will Make You Stand Out

• 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.

Not explicitly mentioned in the job listing. However, typical benefits for such a role in a growth-stage startup include competitive salary, equity options, health insurance, paid time off, and professional development opportunities.