Generative Artificial Intelligence Lead Engineer

Must HaveGenerative AI, RAG, LLM, Java Development tools, MCP, Azure dev, DataBricksGood to haveAbility to understand the pain areas in standard Java development platform & suggest GenAI tool intervention.Ability to provide technical solution of the above toolsAbility to develop & test above GenAi tools and integrate them in regulated enterprise environment.Description Design and deliver hands-on workshops for developers on GenAI-assisted coding, testing, and documentation; building progressive learning paths (Chat → Prompt Engineering → SDD → Full SDLC Integration) with stack-specific materials across Java, Lit, Oracle, and PostgreSQL.Evaluate, configure, and standardise GenAI tooling across teams (IDE extensions, CLI agents, code review assistants); developing reusable spec templates, prompt libraries, and SDD workflows per stack, with conventions for AI-generated artifacts (specs, tests, migrations, documentation).Enterprise Engagement partner with engineering leads to embed SDD practices into existing team workflows and CI/CD pipelines; running pilot programmes with target teams and advising on governance, security, and IP considerations for enterprise GenAI usage.Establish and nurture an internal GenAI engineering community of practice; facilitating knowledge sharing through demos, office hours, internal blogs, and champion networks, while identifying and mentoring GenAI champions within each team to sustain adoption.Define and track adoption KPIs (e.g., SDD usage rates, developer satisfaction, cycle time impact, code quality metrics); reporting on maturity progression across teams and continuously refining the enablement programme based on quantitative and qualitative data.Partner with leading delivery squads to expand the skills, agents and multi agent approaches with a use case that can be an example to other teamsDevelopment of automated tools and processes for AI augmented delivery - Develop a highly automated process to enable skills, agents & project prompts to be shared, managed and published widely.Collaboration with Teammates – Collaborate with product teams, data engineers, and data scientists to understand SDD and AI development use cases, challenges and improvements.