Director - Generative AI

Director Generative AI

Key Responsibilities :

Customer Solutioning and Pre-Sales :

- Lead discovery with client executives and technical teams to identify high-value GenAI use cases and assess feasibility, data readiness, and ROI.

- Design end-to-end GenAI architectures covering LLM selection, RAG, vector databases, agentic workflows, enterprise integrations, security, and governance.

- Own the technical narrative in proposals, SOWs, and orals, including solution architecture, estimates, staffing, risks, and assumptions.

- Lead executive workshops, technical envisioning sessions, proof-of-concept scoping, and presentations to CIO, CDO, and CTO audiences.

- Advise clients on build-versus-buy decisions, cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI), and model trade-offs involving cost, latency, quality, and privacy.

Technical Leadership and Architecture :

- Define reference architectures and engineering best practices for LLM applications, RAG pipelines, AI agents, prompt engineering, and model orchestration.

- Guide technology selection across LLM frameworks, embedding models, reranking, vector search, and open-source versus proprietary models.

- Establish standards for LLM evaluation, guardrails, observability, LLMOps, and production deployment.

- Review solution designs and technical implementations, resolving complex challenges related to retrieval quality, model performance, scalability, security, and cost optimization.

- Stay current with emerging GenAI technologies and translate them into practical client solutions and reusable accelerators.

Delivery and Practice Growth :

- Provide technical oversight across GenAI engagements to ensure solutions are scalable, secure, production-ready, and aligned with the proposed architecture.

- Partner with engineering, data, cloud, and security teams to guide implementation and resolve technical escalations.

- Identify follow-on opportunities, develop reusable solution accelerators, and contribute to the growth of the GenAI practice.

Required Skills and Experience :

- Strong experience in AI/ML, solution architecture, software engineering, or enterprise technology consulting, with substantial hands-on exposure to Generative AI.

- Strong understanding of LLMs, RAG, embeddings, vector databases, semantic search, and agentic AI architectures.

- Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent technologies.

- Knowledge of enterprise AI platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, or Snowflake.

- Understanding of Python, REST APIs, microservices, cloud-native architectures, and enterprise data integration.

- Experience with LLM evaluation, prompt engineering, guardrails, monitoring, and production deployment practices.

- Strong understanding of AI security, data privacy, governance, scalability, and cost optimization.

- Excellent client-facing communication, technical solutioning, stakeholder management, and team leadership skills.