AI LLM Technology Architecture Sr Mgr

Role and Designation

Qualifications

• Any graduation qualification is listed.
• The role specifies 13 to 16 years of experience in the source posting.
• The detailed requirements specify at least 10 years of product management experience and at least 3 years with AI-native products.
• Experience owning enterprise software products or platforms from concept through launch and scale is required.
• Strong software engineering literacy with Python and SQL, plus knowledge of system design, APIs, data pipelines, and cloud-native architecture, is required.
• The role requires client-facing experience in high-ambiguity environments and the ability to influence senior stakeholders.

Product Direction and Discovery

• Set product vision and define target users, value proposition, and deliberate non-goals.
• Work with client business, technology, and operations leaders to identify high-impact, repeatable GenAI and agentic AI problems.
• Balance customer value, technical feasibility, commercial potential, speed, quality, and strategic fit.

AI Product Engineering

• Define agent behavior, tool use, orchestration, memory, uncertainty handling, and human-in-the-loop escalation.
• Guide architecture for LLM-powered applications using prompt workflow orchestration, tool-calling agents, and RAG patterns.
• Make credible technical decisions and unblock engineering in mission-critical environments.

Delivery and Ownership

• Lead products from customer problem, prototype, and design-partner validation through production and scaled adoption.
• Write product briefs, requirements, decision documents, and acceptance criteria.
• Own adoption, usage, retention, customer value, and product quality after launch.

Commercial and Pod Leadership

• Validate desirability, usability, technical performance, business value, and willingness to pay with design partners.
• Act as the technical and product authority within the RDE pod.
• Guide architecture, coding standards, and engagement quality.
• Mentor product and engineering talent on AI patterns, product judgment, and AI-native ways of working.