AI Technical Lead
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Key skills for this role
Role Overview
Fundamentl is seeking an experienced AI Technical Lead with 15+ years of experience.
The role defines enterprise AI architecture, standards, and technology roadmaps for Generative AI, Agentic AI, LLM, and RAG solutions.
The position aligns AI platforms with enterprise architecture, data, security, and governance principles.
Key Skills for This Role
Full Job Posting
Position Summary
Fundamentl is seeking an experienced AI Technical Lead with 15+ years of experience.
The role defines enterprise AI architecture, standards, and technology roadmaps for Generative AI, Agentic AI, LLM, and RAG solutions.
The position aligns AI platforms with enterprise architecture, data, security, and governance principles.
Key Responsibilities
- Design scalable AI platforms, agentic systems, AI integration patterns, and cloud-native solution architectures.
- Establish architecture governance, technology selection criteria, security controls, and AI design best practices.
- Collaborate with business, engineering, data, security, and enterprise architecture teams.
- Lead architecture reviews, technical assessments, and solution assurance for AI initiatives.
Core Technical Expertise
- Generative AI, LLMs, prompt engineering, RAG, GraphRAG, Agentic AI, multi-agent systems, MCP, A2A, AI guardrails, and Responsible AI.
- AI frameworks include LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, and Microsoft Agent Framework.
- Cloud and AI platforms include Azure OpenAI, Azure AI Foundry, Azure AI Search, AWS Bedrock, OpenAI APIs, Gemini, and open-source LLMs.
- Vector and enterprise integration technologies include Cosmos DB Vector Search, Pinecone, Qdrant, Weaviate, Neo4j, APIs, microservices, and event-driven architectures.
Requirements
- 15+ years of professional experience.
- Experience defining enterprise AI architecture, standards, and technology roadmaps.
- Expertise in Generative AI, Agentic AI, LLM, and RAG solutions.
- Ability to design scalable, secure, and reusable AI platforms.
- Knowledge of AI architecture governance, technology selection, security controls, and design best practices.
- Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or Microsoft Agent Framework.
- Experience with cloud and AI platforms such as Azure OpenAI, Azure AI Foundry, Azure AI Search, AWS Bedrock, OpenAI APIs, Gemini, or open-source LLMs.
- Experience with vector databases and enterprise integration technologies including APIs, microservices, and event-driven architectures.
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