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Mid-senior ML role in metros with broad AI/ML stack increases candidate competition.
Specialized ML, MLOps and LLM skills transfer across industries but require domain expertise.
Multiple mandatory ML/LLM, MLOps, tooling and 6–8 years experience create strict technical filters.
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Lead end-to-end technical delivery of AI/ML projects in Healthcare & Life Sciences, owning implementation, deployment, and operationalization of ML solutions.
Spend 50-75% time writing production-grade code, building prototypes, and designing modular, scalable AI system components with rigorous technical reasoning.
Mentor engineers, lead technical reviews, and engage directly with clients to manage risks and explain architectural decisions clearly.
6 to 8 years of professional experience in Machine Learning, Deep Learning, and Software Engineering with end-to-end ML project delivery.
Strong mastery of Python and SQL; experience with software design patterns, Git, and CI/CD automation.
Experience with advanced ML/DL/NLP techniques including Transformers, CNNs, RNNs, and generative AI stacks such as RAG pipelines, agentic AI frameworks, and Model Context Protocol.
Proficiency in PyTorch or TensorFlow; experience with MLOps tools like MLflow, Kubeflow, SageMaker Pipelines, and cloud platforms AWS or GCP.
Highly technical, hands-on engineer comfortable leading complex ML system design and production coding 50-75% of time.
Experienced in cutting-edge generative AI and agentic workflows, capable of delivering scalable AI architectures with well-documented designs.
Skilled mentor and client communicator with ability to translate technical complexity into clear business value and steer technical teams.