AI Solution Architect - Generative AI/Agentic AI

Role Overview:

Lead the design and implementation of enterprise-scale AI and GenAI platforms and solution designing, helping clients transition from experimentation to production-grade adoption. Lead higher-level tasks such as platform selection, data architecture, agent design patterns, GPU sizing, and business case creation. You will bridge business strategy, enterprise architecture, and AI engineering while advising senior stakeholders on AI transformation initiatives.

Key Responsibilities:

- AI Strategy & Advisory: Translate business challenges into AI-enabled solutions, assess client AI/data maturity, and define transformation roadmaps.

- Solution & Platform Architecture: Design end-to-end architectures, including Agentic AI systems, RAG frameworks, and LLM-based applications.

- AI Engineering & Industrialization: Build production-grade systems, establish MLOps practices, and manage the transition from PoC to enterprise-scale deployment.

- Client & Team Leadership: Lead cross-functional teams and communicate complex technical concepts to business leaders.

Required Skills & Experience:

- AI & GenAI: Proficiency with LLMs (OpenAI, Anthropic, etc.), Agentic AI (LangGraph, CrewAI, Agentforce), and Retrieval-Augmented Generation (RAG).

- Architecture & Platforms: Experience in enterprise and solution architecture, distributed systems, and modern data platforms (e.g., Databricks or Snowflake).

- Cloud & Engineering: Proficiency in AWS, Azure, or GCP; Python development; React, JavaScript, or TypeScript; and CI/CD/MLOps frameworks.

- Leadership: 10-18 years of experience in AI/ML or architecture with strong consulting and stakeholder management skills.