Technical Architect

We are looking for an experienced Generative AI / Agentic AI Architect with 15+ years of overall IT experience and strong expertise in designing and implementing enterprise-grade AI solutions. The ideal candidate will have hands-on experience in Retrieval-Augmented Generation (RAG), Knowledge Graphs, context engineering, multi-agent orchestration, and LLM evaluation, along with expertise in frameworks such as LangGraph, CrewAI, and AutoGen.

Key Responsibilities

• Design and architect scalable GenAI and agentic AI solutions for enterprise applications.
• Build advanced RAG pipelines integrating document retrieval, embeddings, vector databases, and reranking techniques.
• Design and implement Knowledge Graphs to enhance contextual retrieval, reasoning, and knowledge representation.
• Apply context engineering techniques to optimize LLM inputs, memory, context windows, and response quality.
• Develop and orchestrate AI agents and multi-agent workflows using LangGraph, CrewAI and AutoGen.
• Establish LLM evaluation frameworks to measure accuracy, relevance, roundedness, reliability, and performance.
• Implement spec-driven development practices to translate business requirements into structured specifications, workflows, and testable implementations.
• Define AI architecture standards, reusable components, security controls, and responsible AI practices.
• Collaborate with business stakeholders, engineering teams, and enterprise architects to deliver production-ready AI solutions.
• Mentor engineering teams and provide technical leadership across GenAI and agentic AI initiatives.

Required Skills

• 15+ years of overall IT experience, with substantial experience in AI/ML, Generative AI, or enterprise solution architecture.
• Strong hands-on expertise in RAG, Knowledge Graphs, and context engineering .
• Experience designing and implementing agentic AI systems and multi-agent orchestration .
• Hands-on experience with LangGraph, CrewAI, and AutoGen .
• Strong understanding of LLM evaluation, benchmarking, observability, and continuous quality improvement.
• Experience with spec-driven development, structured requirements, and automated validation.
• Strong understanding of LLMs, embeddings, vector databases, semantic search, prompt engineering, and retrieval optimization.
• Knowledge of enterprise integration patterns, API design, scalability, data security, and deployment best practices.
• Excellent architecture, problem-solving, communication, and stakeholder management skills.

Preferred Qualifications

• Experience deploying GenAI solutions in production environments.
• Familiarity with cloud-based AI platforms, model serving, and MLOps/LLMOps.
• Experience with Python, REST APIs, vector search technologies, and graph databases.
• Experience leading technical teams and driving enterprise AI adoption.