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Mid-level GenAI role, metro locations, and broad cloud/ML requirements create high candidate density.
Core ML/AI skills transfer across industries, but healthcare compliance and domain knowledge increase specificity.
Explicit 5–8 years plus mandatory LLM, MLOps, and cloud deployment skills make filters strict.
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Design, develop, and deploy generative AI solutions including LLM-based applications, embeddings, and RAG pipelines.
Build and optimize machine learning, deep learning, and NLP models addressing complex business problems and healthcare domain use cases.
Implement production-grade AI services with microservices, APIs, containerization, cloud deployment, and MLOps workflows including CI/CD and automated monitoring.
5 – 8 years of relevant work experience in AI/ML engineering, data engineering, or related roles.
Engineering degree (BE/ME/BTech/MTech/BSc/MSc) mandatory.
Strong skills in ML model building, deep learning, GenAI/LLM solutions (including embeddings, vector stores, RAG workflows), and NLP applications.
Experience with Python, SQL, cloud data platforms (Azure/AWS/GCP), DevOps CI/CD, containerized deployment, and operationalizing scalable AI models.
Experienced in handling large datasets, feature engineering, and building reliable training and inference pipelines at scale.
Able to collaborate across multiple stakeholder groups including product, architecture, clinical SMEs, and engineering teams to deliver enterprise AI solutions.
Has domain knowledge or exposure to healthcare data standards (FHIR/HL7), healthcare analytics, or regulated environments (HIPAA compliance).