Elevance Health - Senior Software Engineer III - AI/ML Models

Job Title : Senior Software Engineer III - AI

Requirement Type : Full-Time Employee

Job Location : Bangalore and Gurugram

Primary Skill : Microservices LLM, Python, FastAPI, Vector DB(Qdrant, Chromadb, stc), RAG, MLOps & Deployment, Cloud, Agentic AI Framework, Kubernetes

ABOUT ELEVANCE HEALTH :

Elevance Health is a leading health company in America dedicated to improving lives and communities and making healthcare simpler. It is the largest managed health care company in the Blue Cross Blue Shield (BCBS) Association serving more than 45 million lives across 14 states.

A regular in Fortune 500 list, Elevance Health ranked 20 in 2022. Gail Boudreaux, President and CEO of Elevance Health has been a consistent name in the Fortune list of most powerful women and currently holds 4th rank on this list.

JOB RESPONSIBILITY :

• Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions.

• Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization.

• Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models.

• Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements.

• Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management.

• Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows.

• Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments.

• Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment.

• Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development.

• Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC.

• Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs.