Lead AI Engineer - Agentic Engineering

We are looking for a  Lead AI Engineer  to help shape and build the next generation of  Agentic AI and AI-powered engineering systems  at Blend360. This is  not a traditional GenAI or chatbot development role . We are looking for an experienced software/AI engineer who understands how to build  production-grade agentic systems  and, importantly, how to leverage  Agentic Engineering as part of the Software Development Lifecycle (SDLC) . You will work across AI engineering, software architecture, agent orchestration, LLM applications, developer productivity, and AI-assisted software development. You will help establish engineering practices around  AI agents, context engineering, tool use, evaluations, autonomous task execution, and AI-augmented development workflows . The ideal candidate combines strong software engineering fundamentals with hands-on experience building and operating real-world Agentic AI systems. What You'll Do Agentic Engineering & AI-Augmented SDLC • Drive the adoption of  Agentic Engineering practices across the software development lifecycle , using AI agents to augment and automate engineering workflows. • Leverage tools and approaches such as  Claude Code, Claude Code Skills, PI, Hermes Agent , and comparable AI coding/engineering agents as part of day-to-day software development. • Build AI-assisted workflows covering  requirements analysis, code generation, code understanding, refactoring, testing, debugging, documentation, code review, and deployment . • Design agent workflows capable of understanding large codebases, managing context, using tools, executing multi-step engineering tasks, and recovering from failures. • Establish best practices around  context management, context engineering, tool calling, agent orchestration, guardrails, human-in-the-loop workflows, and autonomous task execution . • Design and implement  Evals  to measure agent correctness, reliability, code quality, task completion, regression, and overall effectiveness. • Continuously evaluate emerging agentic coding tools and techniques and identify opportunities to improve engineering productivity and software quality. Production-Grade Agentic AI • Architect and develop  multi-agent and agentic systems  capable of performing complex, multi-step tasks in production environments. • Design agent architectures involving  planning, reasoning, tool use, memory/context, execution, reflection, validation, and error recovery . • Build agents that integrate with APIs, databases, enterprise systems, developer tools, and other external services. • Develop reliable  tool-use and MCP-based integrations  where appropriate. • Build production-grade LLM applications using frameworks such as  LangGraph, LangChain, or equivalent orchestration frameworks . • Implement RAG, semantic search, vector retrieval, structured outputs, and other LLM application patterns where required. • Establish appropriate  observability, evaluation, monitoring, security, and guardrails  for agentic applications. Software Engineering & Architecture • Provide technical leadership across the design and development of AI-powered software products and platforms. • Apply strong software engineering principles including  system design, modular architecture, API design, scalability, reliability, testing, CI/CD, and maintainability . • Build production-quality services and APIs using technologies such as  Python, FastAPI, Docker, Kubernetes, and cloud platforms . • Work closely with engineering, product, data, and client teams to translate complex business problems into scalable technical solutions. • Conduct technical design reviews and provide mentorship to other AI/software engineers. • Establish engineering standards and best practices for building AI and age