Ai Architect

Job Requirements Role: AI Architect (Agentic Solutions & Engineering Enablement)

Experience: 10–12+ years in software engineering, with 3+ years focused on enterprise AI/ML and Generative AI systems.

Core Focus: Designing multi-agent orchestration frameworks and driving AI-driven productivity across engineering organizations

• Agentic AI Architecture & Design

Design and implement enterprise-grade, single-agent and multi-agent systems capable of autonomous reasoning, planning, memory management, and task decomposition

Build advanced Retrieval-Augmented Generation (RAG) pipelines and integrate tools/APIs via Model Context Protocol (MCP) or similar standards.

Select and leverage leading agentic frameworks (e.g., LangGraph, Semantic Kernel, AutoGen, or native cloud agent SDKs) matching business requirements. Implement production-ready guardrails, human-in-the-loop checkpoints, and agent observability (AgentOps).

• Enabling Engineering Teams (AI Enablement)

Champion the adoption of AI coding assistants, automated testing tools, and internal developer platforms across engineering units.

Build reference architectures, reusable components, and secure sandbox environments so engineering teams can safely deploy AI capabilities.

Establish governance models, compliance guardrails, and secure workflows for AI-assisted software development and delivery.

Mentor engineers and tech leads on prompt engineering patterns, LLM orchestration, and safe code generation practices.

• Enterprise Integration & Governance

Ensure alignment with enterprise cloud platforms (AWS, Azure, or GCP), data security policies, and responsible AI frameworks.

Oversee MLOps/LLMOps pipelines, ensuring scalability, cost optimization, and robust telemetry monitoring of deployed agents.

Required Qualifications & Skills Technical Expertise: Deep hands-on proficiency in Python, modern LLM APIs (OpenAI, Anthropic, open-source models), and vector databases.

Agentic Frameworks: Demonstrated experience with orchestration libraries like LangChain, LangGraph, or Semantic Kernel.

Cloud & Infrastructure: Familiarity with cloud-native AI services (Azure AI Foundry/OpenAI, AWS Bedrock, or Google Cloud Vertex AI).

Work Experience 10-15Years