AI-ML Engineer

• Design, implement, test, and deploy agentic AI systems and multi-agent workflows to solve complex customer problems
• Lead technical discussions, drive architecture decisions, and mentor the technical team
• Architect and integrate agent orchestration frameworks with tool/API ecosystem
• Own and deliver major solution areas end-to-end with minimal oversight

• 5-7+ years building production-grade ML or GenAI systems; advanced Python
• Hands-on experience with agentic frameworks (Strands, LangGraph, CrewAI or similar)
• Strong prompt engineering and context engineering (grounding, RAG, memory patterns); MCP/A2A experience
• Cross-account IAM architecture experience - designing and implementing IAM roles, trust policies, and permission boundaries for multi-agent systems that span AWS accounts
• API integration experience (consumer/client); IaC using Terraform or CloudFormation

AWS Bedrock (Models, Guardrails); Lambda, API Gateway, S3, IAM, KMS, Secrets Manager, ECR, Basic VPC Should Have

• Bedrock AgentCore Services; CloudWatch, DynamoDB, EventBridge, Step Functions, ECS Fargate
• Agent evaluation experience (automated scoring, performance tracking)
• Inter-agent communication patterns (MCP, A2A) in multi-account environments - cross-account invocation, service control policies (SCPs), and resource-based policies for agent-to-agent access
• API Security (OAuth2, ApiKey, BasicAuth)
• Observability (logging, metrics, tracing); CI/CD pipeline setup
• Vector databases and retrieval patterns
• GenAI coding assistants (Kiro, Q Developer)
• Legacy technology familiarity (Java/Spring, Sybase, DB2, or COBOL) as source context for agentic modernization or integration pattern design
• Financial services or regulated-industry experience (compliance-aware delivery: trade surveillance, data controls, audit requirements)

Large-scale portfolio delivery experience - reusable patterns or components applied across 50+ applications, including wave planning and throughput measurement