Contribute to pragmatic AI architectures such as RAG, agents, copilots, and automation integrated into cloud-native microservices and enterprise data platforms
Support decisions on when to apply RAG, agents, or conventional approaches
Implement and apply spec-driven development specs and templates, and support governance and quality gates
Build and maintain agentic use cases, run and write agents, and implement guardrails defined by the program
Deliver production solutions including LLM integrations, retrieval, and embeddings or vector stores embedded into Java and Spring Boot services
Implement observability, rollout and versioning, and CI/CD for prompts, specs, and models
Deploy and operate solutions on AKS and Azure hosting with guidance from platform and SRE teams
Contribute to tracking adoption, productivity, quality, and risk KPIs for AI-enabled delivery
Participate in playbooks, training, documentation, and team mentoring, and share patterns across squads
Implement responsible AI controls, data handling constraints, and agent guardrails in line with engineering standards