Back
Tier-1 brand, popular AI title, mid-level experience band, and metro location increase applicant competition.
Strong ML/LLM skills are transferable, but healthcare privacy and regulatory needs moderately limit cross-industry fit.
Explicit 5–8 years, mandatory LLM/Generative AI, cloud, security and regulatory requirements create strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and optimize production-ready AI/ML and multi-agent systems including NLP applications and RAG pipelines handling sensitive healthcare data.
Architect and implement scalable, secure AI systems ensuring compliance with HIPAA, GDPR, and enterprise privacy/security standards.
Develop prompt engineering, CI/CD pipelines, monitoring/observability, and ensure model integration with enterprise data for business-driven AI workflows.
5–8 years of experience in ML/NLP, applied AI, or data engineering with at least 2–3 years on Generative AI or LLM-based solutions.
Proficiency with NLP transformer models and frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
Experience deploying production AI/ML solutions on major cloud platforms (AWS preferred) with CI/CD, containerization, and microservices expertise.
Bachelor’s or Master’s degree in Computer Science, AI, ML, Data Science, or related field.
Experienced in building and operating secure, scalable AI systems in regulated healthcare or related environments following strict data privacy and security standards.
Skilled in integrating advanced Generative AI frameworks and vector search technologies for production use cases with robust observability and error handling.
Capable of collaborating across cross-functional teams including product, engineering, cybersecurity, and regulatory to identify risks and deliver compliant AI solutions.