AI/ML Engineer

AI/ML Engineer

We are seeking an experienced AI/ML Engineer to design, develop, and deploy production-ready machine learning and generative AI solutions. This hands-on role focuses on LLM-powered applications, agentic AI systems, RAG pipelines, and document intelligence, with an emphasis on accuracy, scalability, observability, and responsible AI practices.

Responsibilities:

• Build and deploy LLM-powered applications, AI agents, multi-agent workflows, and MCP-based integrations.
• Develop and optimize RAG pipelines using embeddings, vector databases, retrieval, and reranking techniques.
• Create document intelligence solutions for extracting, classifying, and processing complex clinical and operational documents.
• Implement model evaluation, human-in-the-loop validation, monitoring, and AI quality and security controls.
• Develop production-grade Python services and ML pipelines using cloud platforms, containers, and CI/CD tools.
• Collaborate with product, clinical, and operations teams to translate business needs into reliable AI solutions.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent experience.
• 5+ years of experience building and deploying AI/ML solutions, including recent hands-on LLM or agentic AI development.
• Strong expertise in Python, machine learning, deep learning, NLP, and transformer architectures.
• Experience with LLMs, RAG, vector databases, agentic frameworks such as LangChain or LangGraph, and tool/function calling.
• Experience with Azure AI/Azure OpenAI or comparable AWS or GCP AI services.
• Knowledge of SQL, data pipelines, model evaluation, MLOps, Docker, and CI/CD.
• Understanding of Responsible AI, model governance, AI security, and production monitoring.

Preferred Qualifications:

• Experience in healthcare, insurance, or other regulated industries.
• Familiarity with MCP, Kubernetes, Terraform, and AI observability tools.
• Experience building human-in-the-loop workflows and collaborating with clinical or operational subject matter experts.