ML Engineer – GenAI​/LLM​/Azure

Overview

We are seeking an experienced ML Engineer with strong expertise in Azure
, Generative AI
, and Large Language Models (LLMs) to join a high-performing AI engineering team delivering enterprise-scale intelligent solutions.

The ideal candidate will have hands‑on experience in designing, deploying, and optimizing AI/ML systems, with particular focus on GenAI applications,
RAG architectures
, model lifecycle management, and scalable MLOps practices.

• Design, develop, and deploy scalable AI/ML solutions using Azure cloud technologies
• Build and optimize LLM‑based applications and Generative AI solutions
• Develop Retrieval‑Augmented Generation (RAG) pipelines integrating vector databases and enterprise data sources
• Fine‑tune pretrained LLMs using PEFT methodologies including LoRA and QLoRA
• Design and maintain robust ETL/ELT data pipelines for AI model training and inference
• Implement AI model monitoring, performance tuning, versioning, and lifecycle management
• Build and manage automated CI/CD pipelines for model deployment and retraining workflows
• Collaborate closely with Data Scientists, Dev Ops Engineers, and business stakeholders during the end‑to‑end model development lifecycle
• Deploy containerized AI applications using Docker and Kubernetes
• Ensure AI solutions comply with Responsible AI principles including fairness, transparency, governance, and security standards
• Support infrastructure provisioning and optimization across cloud‑based AI environments
• Maintain technical documentation and contribute to best practices for scalable AI engineering

• 5+ years of experience in Machine Learning Engineering or AI Engineering
• Strong hands‑on experience with Microsoft Azure
• Proven experience working with Large Language Models (LLMs) and Generative AI solutions
• Experience building and deploying RAG architectures
• Expertise in MLOps
  , CI/CD pipelines
  , and model deployment strategies
• Experience with Docker and Kubernetes
• Strong Python programming skills
• Experience with model monitoring, observability, and performance optimization
• Familiarity with vector databases and embedding workflows
• Strong understanding of AI governance and Responsible AI practices

• Experience with in the Insurance domain
• Exposure to Agentic AI systems and autonomous AI workflows