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Mid-level, popular ML/AI role in metro hubs, hybrid remote and generalist production requirements increase applicant density.
Core ML and LLM engineering skills transfer across industries, but healthcare domain knowledge increases domain specificity.
Explicit 6-9 years plus proven production LLM/AI and evaluation expertise make filters stringent.
Design, develop, and continuously improve production AI systems focused on healthcare workflows like clinical documentation, medical coding, denial management, and revenue cycle automation.
Own end-to-end AI solution lifecycle including problem framing, agent design, evaluation, deployment, failure analysis, and iterative improvements to ensure reliability and auditability in clinical environments.
Lead technical direction across multiple projects, mentor engineers, and collaborate with research, ML engineering, platform, and product teams to productionalize AI capabilities effectively.
6-9 years of applied AI, ML engineering, research engineering, or software engineering experience with production system deployment experience.
Bachelor's degree in Computer Science, Engineering, Mathematics, or related quantitative field (Master's or PhD preferred but not mandatory).
Experience building, evaluating, and improving AI or ML models, preferably with exposure to healthcare or regulated domains (healthcare experience is a plus, not mandatory).
Job location: Hyderabad (Hybrid) or Bengaluru (Remote).
Strong engineering mindset combined with applied scientific rigor, capable of measuring model performance and reliability in ambiguous, real-world healthcare data settings.
Experienced in bridging the gap between research demos and stable production AI systems in high-stakes or regulated environments.
Proactive owner of AI system outcomes who understands workflow intricacies and drives continuous system improvements with measurable business impact.