Cloud infrastructure (ideally AWS) with AI

Cloud infrastructure (ideally AWS) with AI

Requirement 
Proficiency building and operating on cloud infrastructure (ideally AWS): containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation). 
Significant software development experience in one or more languages (Python, C/C++, Go, Java); strong hands-on large-scale Python experience preferred. 
Three or more years of designing, architecting, testing, and launching production ML systems (deployment/serving, evaluation, monitoring, data pipelines, fine-tuning workflows). 
Practical LLM experience: API integration, prompt engineering, fine-tuning/adaptation, RAG, and tool-using agents (vector retrieval, function calling, secure tool execution). 
Understanding of commercial and open-source LLMs and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude). 

Focus Area 
Description 
Build agentic AI systems 
Design and implement tool-calling agents combining retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following the MCP protocol; engineer guardrails for safety, compliance, and least-privilege access. 
Productionize LLMs 
Build evaluation frameworks for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops for production operations. 
Integrate with runtime ecosystems 
Connect agents to observability, incident management, and deployment systems for automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability. 
Collaborate with users 
Partner with production and application teams to translate pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost. 
Enhance governance 
Build validator models, adversarial prompts, and policy checks; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations. 
Improve scale & performance 
Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet SLOs.