Associate Director, Generative AI

Overview As the Senior Manager of AI Engineering, you will lead the architectural vision and engineering execution for our Agentic AI ecosystem. You aren't just building agents; you are building the platform and the high-performing team that enables autonomous, multi-step problem solving at scale. You will bridge the gap between high-level business transformation and deep technical implementation, serving as a key strategic partner to executive leadership Responsibilities Core Responsibilities Strategic Leadership & Roadmap (35%) Visionary Orchestration: Define the long-term technical strategy for "Agent-to-Agent" (A2A) communication and cross-domain AI workflows. ROI & Unit Economics: Own the "Build vs. Buy" decisions. Balance model performance with latency and cost-per-token, ensuring AI initiatives deliver measurable business value. Strategic Roadmap: Transform broad business transformation goals into a concrete, phased engineering roadmap leveraging GenAI and Agentic frameworks. Engineering Excellence & Architecture (30%) Architectural Governance: Oversee the design of scalable RAG pipelines, tool-calling protocols, and memory management systems. Reliability & Evaluation: Establish "Production-Grade" standards. Implement rigorous evaluation frameworks (e.g., hallucination detection, faithfulness metrics) and automated testing for non-deterministic AI outputs. Observability: Lead the integration of advanced monitoring to track agent reasoning traces, ensuring end-to-end transparency in autonomous decision-making. 3. People & Organizational Impact (25%) Team Scaling: Recruit, mentor, and lead a high-performing squad of AI Engineers and potentially junior managers.Foster a culture of "Psychological Safety" for rapid experimentation. Cross-Functional Influence: Act as the primary technical liaison for Data Science, Security, and IT. Translate complex agentic "reasoning chains" into clear business logic for non-technical stakeholders. Vendor & Ecosystem Management: Manage relationships with foundation model providers (OpenAI, Anthropic, Google) and framework partners. Innovation & Continuous Learning (10%) Frontier Research: Stay ahead of the curve in Model Context Protocol (MCP), small language models (SLMs) for edge agents, and new reasoning frameworks (e.g., ReAct, Reflexion). Qualifications Education: Master’s or PhD in Computer Science (AI/ML concentration) or equivalent experience. Experience: 10+ years of total experience in Software/AI Engineering. 3+ years in a formal leadership/management role, ideally managing other leads or senior ICs. Proven track record of taking Agentic AI or LLM-based applications into a high-scale production environment (not just internal tools). Technical Mastery: Frameworks: Mastery of LangGraph, CrewAI, AutoGen, or Haystack. Orchestration: Deep understanding of workflow decomposition, tool-calling logic, and state management. DevOps/AIOps: Experience with CI/CD for LLMs and vector database optimization (Pinecone, Weaviate, etc.). Cloud: Professional-level architecture experience in AWS (Bedrock), Azure (OpenAI Service), or GCP (Vertex AI). Soft Skills: Executive Presence: Ability to present "Agentic ROI" to the C-suite. Agility: Comfort with the 2-week "breakthrough cycle" of the current AI market.