Manager, Machine Learning Engineer - Applied AI

• 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
  
  

About Autonomize AI

Autonomize AI is revolutionizing healthcare by streamlining knowledge workflows with AI. We reduce administrative burdens and elevate outcomes, empowering professionals to focus on what truly matters — improving lives. We're growing fast and looking for bold, driven teammates to join us.

The Opportunity

We're hiring an Manager, Machine Learning Engineer - Applied AI to program‑manage a portfolio of ML initiatives—from LLM/RAG workflows to clinical NLP and MLOps. You'll create clarity in ambiguity, orchestrate cross‑functional execution, and own outcomes end‑to‑end. This role is perfect for a technical program/people leader who can go deep on the work while driving scale—a driver, not a passenger—and who thrives on aligning fast and executing faster.

Key Responsibilities

Program & Portfolio Leadership

• 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.
  
  

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.
  
  

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.