Director of Product - AI Platform

Director of Products, AI Platform Location: Bengaluru, India Department: Product Management About the Role We are looking for a Director of Products for our AI Platform to own the product strategy, roadmap, and delivery for Aurigo's proprietary AI foundation model, agent framework, and the full AI platform layer that powers all of our products. This is a deeply technical product leadership role that sits at the intersection of AI research, enterprise software, and capital program domain expertise. You will work directly with engineering and data science teams to define what gets built, how it is evaluated, and how it is delivered safely into consequential enterprise workflows. You will also engage directly with customers and the go-to-market team to ensure the AI platform story is grounded in real-world use cases and buyer value. This role requires someone who understands not just AI product management but the technical underpinning of modern AI systems well enough to make credible decisions about architecture, evaluation, and responsible deployment. Roles and Responsibilities Own the product strategy and roadmap for the AI platform including the foundation model, ingestion and context layers, orchestration layer, agent framework, model manager, and the Copilot experience across all products. Define and drive the AI product development lifecycle from research and prototyping through evaluation, production deployment, and continuous improvement. Work closely with data science, engineering, and ML teams to translate business and customer requirements into AI product decisions, and to ensure the team is building the right things at the right level of quality. Own the evaluation and observability framework for all AI products including evals design, benchmark definition, regression testing, and responsible AI guardrails. Ensure every agent and model deployment has defined success criteria and a measurement plan before it ships. Partner with the Sr PMs across all product lines to ensure AI capabilities are embedded natively into product workflows and not bolted on as standalone features. Drive the model management strategy including model selection, provider evaluation, cost optimization, and the framework for switching providers without disrupting agent logic. Define the MCP server strategy and own the roadmap for expanding the set of tools and integrations available to agents. Engage directly with enterprise customers to understand their AI adoption journey, gather feedback on agent performance, and translate those insights into roadmap decisions. Represent the AI platform in customer engagements, partner discussions, and investor conversations where the technical depth of the AI strategy is on the table. Build and maintain a strong understanding of the competitive AI product landscape including foundation model providers, agent frameworks, and enterprise AI competitors. Soft Skills Technically credible with engineering and data science teams without needing to write code. Can engage deeply on architecture decisions, model tradeoffs, and evaluation design. Strong external-facing presence. Comfortable presenting AI product strategy to enterprise buyers, regulators, and investors with clarity and intellectual honesty. Able to translate complex AI concepts into clear product narratives for non-technical stakeholders including sales, customer success, and executive audiences. High ownership mindset. Takes accountability for AI product quality, safety, and customer outcomes, not just feature delivery. Intellectually curious and stays current with fast-moving developments in foundation models, agent architectures, and enterprise AI deployment patterns. Hard Skills Deep understanding of the modern AI product development lifecycle including data pipelines, model training and fine-tuning, prompt engineering, RAG architectures, and agentic system design. Proven experience designing and running AI evaluation frameworks including offline evals, human evaluation, A/B testing, and production monitoring for AI systems. Strong understanding of responsible AI principles including fairness, auditability, human-in-the-loop design, and guardrail implementation in enterprise contexts. Experience with model context protocol or equivalent tool-use frameworks for agentic systems. Ability to define and track AI-specific product metrics including model accuracy, latency, cost per inference, hallucination rates, and task completion rates. Technical Skills Familiarity with major foundation model providers and the tradeoffs between them across capability, cost, latency, and data privacy. Working knowledge of vector databases, semantic search, and embedding-based retrieval systems. Familiarity with AI evaluation tools and frameworks such as LLM eval libraries, RAGAS, or equivalent tooling for measuring AI system performance. Understanding of AI orchestration patterns including ReAct loops, multi-agent system