7-9 years of experience in solution architecture, application architecture, or senior technical design roles.
Experience designing and implementing cloud-based solutions (AWS Mandatory)
Production experience with AWS Bedrock — model invocation, bedrock guardrails configuration, AI observability, RAGAS evaluation, and knowledge base integration
Hands-on experience building multi-agent workflows using LangGraph, CrewAI, AWS Bedrock Agents, or AgentCore
Working knowledge of AWS orchestration services — Step Functions, EventBridge, SQS — for agent workflow coordination
Designed and implemented RAG and data ingestion pipeline— chunking strategy, vector store (OpenSearch, Pinecone, pgvector) selection, retrieval quality tuning
Experience with FastAPI or Flask for building AI microservices, exposure to React, JavaScript UI design
Knowledge of containerization — Docker, Kubernetes, Amazon ECS/EKS
Proficiency in AWS CDK or Terraform for infrastructure as code
Familiarity with CI/CD pipelines — GitHub Actions, AWS CodePipeline, Jenkins
Strong understanding of data engineering concepts — ETL, data lakes, streaming (Kinesis)
Experience working with AI/ML, NLP, or LLM-based technologies in enterprise environments
Architecture and designing event-driven, microservices, and serverless architectures, security (Cognito, oAuth2, OIDC), KMS
Strong understanding of secure, scalable, and compliant system design principles.
Ability to work effectively in a matrixed, cross-functional enterprise environment.
Strong written and verbal communication skills.
Preferred / Nice-to-Have Skills
Familiarity with Bedrock Model Evaluation and A/B testing frameworks
Exposure to other cloud AI platforms — Azure OpenAI, Google Vertex AI
Experience with OpenAI, Anthropic SDK, or Cohere APIs beyond AWS
Knowledge of graph databases (Neptune, Neo4j) for knowledge graph-powered AI
Exposure to other agentic AI frameworks — AutoGen, CrewAI, Bedrock multi-agent collaboration