Pradeep S.

MLOps Engineer | Deploying AI Agents to Production AWS Infrastructure

I take AI agents and LLM applications from working code to deployed, observable, production infrastructure. My focus is the deployment stack built specifically for agentic AI workloads: LangGraph agents wrapped in FastAPI, containerized with Docker, deployed on AWS, and monitored with AI-specific observability. I come from a Senior DevOps and AWS Solutions Architecture background, with hands-on experience in Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, GitLab CI), zero-downtime deployments and migrations, and FinOps-driven cloud cost optimization. I've worked with startups, SaaS companies, and growing engineering teams that needed reliable, cost-efficient cloud infrastructure. AGENT DEPLOYMENT PIPELINE My core pattern: LangGraph agents wrapped in FastAPI, containerized with Docker, deployed via ECS, Lambda, or EC2 depending on the workload - ECS for always-on agents, Lambda for event-driven workloads, EC2 for more runtime control. Same container runs identically in dev, staging, and production. MANAGED MODEL HOSTING Bedrock Runtime and Bedrock AgentCore where a client doesn't want to manage their own model infrastructure - models run on managed AWS infra with IAM-based access control, no GPU provisioning needed on their end. ML PLATFORM SageMaker for clients doing actual model training or fine-tuning - a repeatable, versioned path from training data to a deployed model endpoint, not a one-off manual run. ORCHESTRATION ECS, Lambda, and Step Functions for multi-step agent workflows - Step Functions specifically for branching logic, retries, and resumability when a workflow calls multiple tools in sequence. OBSERVABILITY Two layers on every deployment: LangSmith for AI-specific tracing, prompt evaluation, and hallucination detection, and CloudWatch for infra-level monitoring and alerting - so you know if the service is degraded, not just if the AI's output looks off. CONTAINERIZATION Docker workloads on ECS or Fargate, with auto-scaling and zero-downtime deployments so agent updates don't interrupt service. MODEL SERVING (EDGE/LOCAL) Ollama for latency-sensitive or data-privacy-sensitive cases needing local or private-network model serving. HOW I DEPLOY AI SYSTEMS - Containerize agent code to run identically across dev, staging, production - Design around cost and reliability, not just "does it run" - Build tracing and hallucination detection in before real users see it - Apply SOC 2 / HIPAA / GDPR-aware practices to AI/LLM deployments from day one IDEAL ENGAGEMENTS Deploying AI agents from prototype to production - MLOps pipeline setup for LangChain/LangGraph - AWS infra for LLM workloads (Bedrock, SageMaker, ECS) - Observability for RAG/agent systems - AI infrastructure cost optimization MY PROCESS - Assess existing agent code for production gaps (error handling, secrets, scaling) - Containerize, then deploy to the right AWS target with IaC for reproducibility - Wire up observability before go-live, not after an incident - Right-size and review costs so the deployment stays affordable as usage grows WHY WORK WITH ME - Production-infra discipline where most people stop at "it runs on my machine" - Security, compliance, and cost control built in from day one - AWS Certified Solutions Architect – Professional, hands-on across EC2, ECS, Lambda, RDS, DynamoDB, Bedrock