Requirements
- Proven experience in designing and implementing AI-driven solutions within software development environments (e.g., AI-assisted coding, testing automation, DevOps optimization).
- Strong understanding of the software development lifecycle (SDLC) and ability to identify high-impact areas for AI enablement.
- Hands-on experience with LLMs, GenAI, and agentic workflows (e.g., AI agents for code generation, test creation, documentation, or release automation).
- Ability to define, lead, and coordinate AI adoption processes, from strategy through pilot execution to scaled rollout.
- Experience in evaluating and selecting AI tools and platforms (e.g., Copilot, code assistants, AI testing tools, orchestration frameworks), including cost–benefit and ROI analysis.
- Strong analytical skills to identify and prioritize use cases with the highest business value (e.g., agentic development candidates, automation opportunities).
- Experience with integration of AI solutions into existing development ecosystems (e.g., Azure DevOps, CI/CD pipelines, code repositories).
- Familiarity with data architecture concepts, including RAG, embeddings, vector databases, and knowledge grounding.
- Understanding of software quality, security, and compliance considerations (e.g., code quality, GDPR, secure AI usage).
- Ability to work across technical and business domains, translating needs into practical AI implementation plans.
- Strong stakeholder management and communication skills, with experience driving cross-functional initiatives.
Nice to Have
- Experience with AI governance frameworks and responsible AI practices.
- Background in agent-based architectures and orchestration tools (e.g., LangChain, Semantic Kernel, AutoGen).
- Knowledge of test automation frameworks and AI-driven QA (e.g., E2E, synthetic data, test generation).
- Experience in change management and AI adoption within enterprise organizations.
