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Tier-1 brand, mid-level experience band, and metro location create high candidate density despite niche GenAI skills.
Generative AI and agentic engineering skills are transferable but finance-specific production and compliance needs raise sensitivity.
Multiple mandatory GenAI, agentic, production, and tooling requirements make screening highly stringent.
Design and architect advanced generative and agentic AI solutions integrated into enterprise Controls Technology platform.
Develop and optimize context engineering strategies, retrieval-augmented generation (RAG) systems, knowledge graphs, and multi-agent orchestration for scalable, reliable AI applications.
Ensure robust production deployment with governance, observability, compliance, and mentor junior developers in AI best practices.
Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field.
5–7 years of experience in AI/software development, including significant experience in Generative AI and agentic AI.
Hands-on expertise with generative AI concepts, prompt/context engineering, RAG systems, knowledge graphs, and multi-agent AI frameworks.
Experience with cloud infrastructure (AWS or equivalent), containerization (Docker), orchestration (Kubernetes), and CI/CD for AI applications.
Experienced in architecting and deploying advanced generative and agentic AI solutions at enterprise scale with a focus on context and retrieval systems.
Proficient in integrating multi-agent workflows using frameworks like Google ADK and skilled in governance, agent orchestration, and interoperability protocols (MCP, A2A).
Comfortable working in cross-functional teams with a strong technical mentoring role and responsibility for production-grade AI system compliance and observability.