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Strong employer brand, metro location, and mid-level AI role create moderate applicant competition.
Specialized ML/LLM and MLOps skills transfer across industries, but healthcare domain knowledge increases sensitivity.
Explicit 5+ years requirement plus many mandatory ML, MLOps, and infra skills increases filter strictness.
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Lead design, development, and delivery of scalable AI/ML and data engineering solutions within healthcare domain.
Develop and optimize large language model (LLM) inference architectures, including GPU memory optimization, quantization, and prompt engineering for chatbots and semantic retrieval.
Operate Kubernetes and container orchestration with advanced configurations to deploy ML workloads and ensure performance improvements.
Bachelor's degree in Engineering (preferably Computer Science/Engineering or AI/Data Science stream).
Minimum 5+ years relevant experience in AI Engineering; preferred total experience 8-12+ years.
Advanced Python proficiency with data science and deep learning libraries; experience with LLM frameworks (e.g. Hugging Face Transformers, LangChain).
Experience with Kubernetes, container orchestration, microservices architecture, and DevOps tools like Terraform, CI/CD pipelines, and version control (Git).
Experienced in building and deploying enterprise-grade AI/ML and LLM solutions, with deep expertise in model optimization and scaling.
Skilled in both software engineering (microservices, concurrency, test-driven development) and MLOps including containerized deployment and automated model retraining.
Comfortable working in cross-functional teams integrating AI/ML capabilities into healthcare operational workflows and ensuring compliance with healthcare regulations.