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.
Key Responsibilities
- 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, DevOps 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
Required Skills and Experience
- 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
Nice to Have
- Experience within the Insurance domain
- Exposure to Agentic AI systems and autonomous AI workflows