Sr AI ML Engineer

Job Summary

We are seeking a highly skilled and motivated Senior AI Engineer with deep expertise in Generative AI, Large Language Models (LLMs), and cloud-native AI platforms. The ideal candidate will have a solid foundation in AI/ML, hands-on experience with LangChain, LangGraph, and familiarity with AWS Bedrock and Azure AI Foundry. This role involves building secure, scalable, and responsible GenAI solutions while collaborating across teams to drive innovation and impact.

Primary Responsibilities:

• Design, develop, and deploy AI/ML and Generative AI models for predictive, prescriptive, and generative analytics across healthcare datasets
• Implement advanced architectures including LLMs (GPT, Gemini, LLaMA), Retrieval-Augmented Generation (RAG), and Agentic Frameworks
• Build and optimize end-to-end pipelines using Python (Sci-kit Learn, Pandas, Flask, LangChain), PySpark, T-SQL and SQL
• Develop and fine-tune multiple GenAI models for NLP, summarization, prompt engineering, and conversational AI
• Apply MLOps best practices: model versioning, drift analysis, quantization, MLFlow, containerization with Docker, and CI/CD pipelines
• Work with cloud platforms: Azure (Databricks, ML Studio, Data Factory, Data Lake, Delta Tables), AWS, and GCP for scalable deployments
• Integrate data warehousing solutions like Snowflake and manage large-scale data pipelines
• Collaborate in an Agile environment, participate in sprint planning, and maintain code repositories using GitHub/Git
• Ensure compliance with security and governance standards for healthcare data
• Coach and mentor junior team members

Technical Skillset

• AI/ML Foundations
  • Design and implement machine learning and deep learning models for classification, NLP tasks
  • Build and maintain end-to-end ML pipelines including data preprocessing, model training, evaluation, and deployment
• Generative AI & LLM Engineering
  • Develop and fine-tune LLM-based applications using LangChain, LangGraph, and other GenAI frameworks
  • Build Multi Agentic workflows and RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases
  • Leverage AWS Bedrock and Google Vertex AI for scalable and production-grade GenAI deployments
• LLM Security & Responsible AI
  • Implement guardrails to prevent prompt injection, reduce hallucinations, and ensure safe model outputs
  • Apply best practices for LLM security, including output moderation, access control, and auditability
  • Ensure compliance with Responsible AI principles - fairness, transparency, and explainability
• Cloud-Native AI Development
  • Deploy and manage GenAI solutions on AWS and Google Suite, utilizing services like Bedrock, SageMaker, Vertex AI
  • Integrate LLMs with enterprise systems using REST APIs, SDKs, and orchestration tools
• Collaboration & Mentorship
  • Work closely with product managers, data scientists, and platform teams to translate business needs into GenAI solutions
  • Mentor junior engineers and contribute to internal knowledge-sharing initiatives

Required Qualifications:

• Graduate degree or equivalent experience
• 10+ years of hands-on experience in AI/ML techniques like Prompt Engineering, RAG (Retrieval Augmented Generation) and Agentic AI
• Hands-on experience with Generative AI frameworks/architectures (LangChain, HuggingFace, OpenAI APIs)
• Solid expertise in Python, PySpark, T-SQL, SQL, and big data technologies (Hadoop, Spark)
• Deep knowledge of statistics, data modeling, and simulation
• Solid understanding of LLM security, prompt engineering, and responsible AI practices
• Familiarity with CI/CD pipelines, GitHub Actions, and containerization tools
• Proficiency in cloud technologies: Azure (Databricks, ML Studio), AWS Bedrock, Azure Foundry, Kafka, and cloud-native AI services
• Proven excellent problem-solving skills and ability to handle ambiguity

Preferred Qualifications:

• Experience with LLMs (GPT, Gemini, LLaMA) and prompt-based learning
• Internal Data management and Big data handling experience
• Knowledge of Kafka, TensorFlow, and advanced deep learning architectures (CNNs, Autoencoders)
• Solid understanding of Agile methodologies and DevOps practices