AI​/ML Engineer

Key Responsibilities 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 Must Have 5-7+ years building production-grade ML or GenAI systems; advanced Python Hands-on experience with agentic frameworks (Strands, Lang Graph, 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 Cloud Formation AWS Bedrock (Models, Guardrails);
Lambda, API Gateway, S3, IAM, KMS, Secrets Manager, ECR, Basic VPC Should Have Bedrock Agent Core Services;
Cloud Watch, DynamoDB, Event Bridge, 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, Api Key, Basic Auth) 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

5-7+ years building production-grade ML or GenAI systems; advanced Python Hands-on experience with agentic frameworks (Strands, Lang Graph, 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 Cloud Formation AWS Bedrock (Models, Guardrails);
Lambda, API Gateway, S3, IAM, KMS, Secrets Manager, ECR, Basic VPC

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