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Tier-1 employer, mid-level (5–8 years), popular AI title, and broad GenAI requirements drive high competition.
Healthcare privacy, HIPAA/GDPR, and sensitive data handling create strong industry-specific fit requirements.
Explicit 5–8 years, mandated Generative AI experience, specific tech stack, and regulatory compliance raise strictness to high.
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Design, build, and optimize production AI/ML systems including NLP applications and multi-agent workflows with routing, planning, error handling, and human-in-the-loop escalation.
Architect and implement secure, scalable AI systems that comply with privacy, security, governance, and regulatory requirements, collaborating with cross-functional teams to implement controls.
Develop retrieval-augmented generation (RAG) pipelines, prompt engineering strategies, observability for LLMs, and automated CI/CD pipelines to deliver reliable AI solutions using enterprise datasets.
5-8 years experience in ML/NLP, applied AI, data engineering or related field, with 2-3 years hands-on experience developing Generative AI or LLM-based solutions.
Practical expertise with transformer-based model development using PyTorch, TensorFlow, or Hugging Face frameworks.
Experience deploying production AI/ML solutions on cloud platforms (AWS or similar) and proficiency with tools like Python, SQL, Docker, Git, and CI/CD pipelines.
Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science, or related discipline.
Experienced AI engineer capable of architecting and delivering complex production AI systems in healthcare technology environments with regulatory compliance (HIPAA, GDPR).
Strong specialization in generative AI, LLM applications, prompt engineering, and multi-agent orchestration frameworks (e.g. LangChain, LlamaIndex).
Proficient collaborator working in SAFe/agile environments and cross-disciplinary teams managing risks, dependencies, and secure, privacy-focused AI system design.