AI/ML Engineer

Role & responsibilities

• Built Domain Agents and RAG pipelines for multi-step reasoning workflows. Integrated LLMs with external tools and APIs for agentic task execution

• Implemented Environment as Code (EaC) practices to automate dev, staging, and prod environments for consistent ML deployments

• Developed CI/CD pipelines using Bitbucket, Jenkins, and Spinnaker for automated build, test, and deploy to AWS EKS

• Managed artifact lifecycle using Artifactory PyPI and Container Registry for versioned, secure ML model distribution

• Deployed scalable inference services using AWS AppConfig, Docker, and Kubernetes (EKS). Used VS Code and Devshell for cloud-native development

• Streamed real-time data using Kafka for low-latency inference and agent coordination

• Implemented Galileo Assurance for model evaluation, explainability (SHAP/LIME), and performance monitoring of LLM agents

• Integrated SmartSDK for agent development and ForgeRock for secure authentication and authorization in AI workflows

• Built observability and monitoring (AOM) pipelines to trace agent reasoning, token usage, and latency using MLflow and CloudWatch

• Automated deployments with Jules and managed infrastructure with Git-based workflows in Agile teams

Preferred candidate profile