Roles and Responsibilities :

- Serve as the primary technical advisor for AI initiatives, helping teams translate business objectives into scalable and supportable AI solutions.
- Guide engineering teams through end-to-end implementation of AI-native architectures including RAG pipelines, AI copilots, multi-agent systems, intelligent workflows, and enterprise AI platforms.
- Develop and maintain enterprise AI reference architectures, implementation guidelines, technical standards, and onboarding documentation.
- Facilitate adoption of AI engineering tools, frameworks, cloud services, and development practices across multiple delivery teams.
- Conduct architecture reviews and technical assessments to ensure AI solutions align with enterprise standards, security requirements, scalability objectives, and responsible AI principles.
- Identify implementation challenges, technology gaps, and operational risks, and recommend solutions that accelerate AI delivery and adoption.
- Establish communities of practice, technical learning programs, and hands-on enablement workshops to build AI engineering capabilities across the organization.
- Collaborate with leadership teams to define AI adoption roadmaps, platform strategies, and investment priorities.
- Measure and report AI adoption metrics, technical enablement outcomes, platform utilization, and business impact achieved through AI programs.

Job Requirements : Drive enterprise-wide adoption of AI, Generative AI, Agentic AI, Machine Learning,



and AI-native technologies by providing technical leadership and implementation guidance.

Establish and promote AI engineering standards, reference architectures, reusable frameworks, best practices, and implementation patterns across technology teams.

Enable product and engineering teams to successfully design, build, deploy, and operate AI-powered applications and intelligent automation solutions.

Provide technical consultation on AI solution architecture, model selection, orchestration frameworks, agent design, RAG implementation, and enterprise system integration.

Develop reusable accelerators, templates, proof-of-concepts, sample applications, and onboarding assets that reduce implementation timelines and increase developer productivity.

Partner with platform engineering teams to define AI platform capabilities, infrastructure standards, governance policies, security controls, and operational best practices.

Support technical teams in up-to-date AI technologies including LLMs, embeddings, vector databases, AI agents, semantic search, AI orchestration frameworks, and cloud AI services.

Enable responsible AI implementation through governance, guardrails, compliance controls, security reviews, and best practices for AI safety and risk mitigation.

Lead technical workshops, architecture reviews, design sessions, hackathons, and knowledge-sharing initiatives to improve organization-wide AI maturity.

Collaborate with vendors and technology partners to evaluate emerging AI technologies, frameworks, and enterprise AI platforms.

📌 AI Enabler (Bengaluru)
🏢 Persistent
📍 Bengaluru