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Remote, popular ML role with broad required toolset increases competition.
ML and MLOps skills broadly transferable, but healthcare/insurance governance increases domain specificity.
Extensive mandatory stack, MLOps, cloud and regulated healthcare requirements make screening strict.
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Design, develop, and deploy enterprise-grade AI/ML applications leveraging Large Language Models and Generative AI technologies.
Build and optimize Retrieval-Augmented Generation pipelines, integrate LLM APIs, and fine-tune foundation models for domain-specific use cases.
Implement MLOps best practices including CI/CD, model versioning, monitoring, and deploy containerized AI services on cloud platforms using Kubernetes.
Strong hands-on experience with Generative AI, LLMs, prompt engineering, RAG, embeddings, vector databases, and model fine-tuning.
Proficient in Python and ML frameworks such as PyTorch or TensorFlow; experience with LangChain, LlamaIndex, Hugging Face Transformers, and REST API development (FastAPI/Flask).
Experience deploying AI/ML solutions on cloud platforms (Azure OpenAI Service, AWS SageMaker, or Google Vertex AI) and using Docker and Kubernetes for containerization and orchestration.
Bachelor's or Master's degree in Computer Science, AI, Data Science, Machine Learning, Mathematics, Statistics, or related field; relevant AI/ML certifications advantageous; Work Experience Required: Not explicitly mentioned in the JD.
Expert in building scalable, production-ready AI/ML pipelines incorporating advanced LLM and generative AI technologies within enterprise environments.
Experienced in implementing MLOps practices and cloud deployment architectures with containerization and orchestration tools to ensure performant and maintainable AI services.
Familiar with regulated domains such as Healthcare and Insurance, emphasizing responsible AI practices, governance, security, and privacy compliance.