Platform Engineer

Roles & Responsibilities:

• Design, build, and deploy AI/ML applications, pipelines, and production-grade ML systems
• Develop scalable microservices-based architectures for model serving using REST and gRPC
• Containerize and deploy ML workloads on Kubernetes (EKS) using Docker and Helm
• Implement event-driven architectures using Kafka and RabbitMQ for real-time data processing
• Optimize model inference performance, latency, and cost efficiency across production systems
• Build and maintain LLM-based applications including RAG pipelines and agentic systems
• Develop and integrate vector search solutions using embeddings and semantic search frameworks
• Implement observability, monitoring, logging, and evaluation pipelines for ML systems
• Ensure secure handling of PII/PHI data with encryption, RBAC, authentication, and authorization
• Collaborate with cross-functional teams to build end-to-end ML platforms and services
• Implement CI/CD and MLOps pipelines using tools like MLFlow, Airflow, and Kubeflow/SageMaker
• Manage cloud-based deployments and infrastructure on AWS with a focus on scalability and FinOps