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 agentic applications.

Leadership & Innovation

• Act as a technical leader for Agentic AI initiatives and influence architecture and engineering decisions across teams.
• Mentor engineers on AI engineering, agentic architectures, software engineering practices, and AI-assisted development .
• Stay current with rapidly evolving AI coding agents, agent frameworks, LLM capabilities, evaluation methodologies, and engineering practices.
• Prototype emerging technologies and transition successful approaches into reliable production solutions.
• Collaborate with clients and internal stakeholders to identify opportunities where Agentic AI can deliver measurable business and engineering value.

Must Have

• 6+ years of software engineering / AI engineering experience , with strong hands-on development experience.
• Strong software engineering fundamentals with experience building production-grade applications and services .
• Demonstrable experience building production-grade Agentic AI systems , beyond simple chatbots or basic RAG applications.
• Strong understanding of Agentic Evaluation / Agent Evals , including designing evaluation frameworks for autonomous and multi-agent systems.
• Experience creating evaluation datasets, test scenarios, metrics, automated regression tests, and quality gates for agentic applications.
• Ability to evaluate agents beyond final-answer accuracy, including planning, tool use, reasoning trajectory, context handling, reliability, safety, latency, cost, and task completion .
• Strong hands-on experience with Python and modern backend/API development.
• Experience with LLMs, GenAI, agent orchestration, tool calling, and RAG .
• Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Google ADK, or equivalent .
• Strong understanding of multi-agent architectures, planning, reasoning, context management, tool use, memory, and agent execution .
• Experience working with Evals / evaluation frameworks to measure and improve AI/agent performance.
• Experience with cloud, containers, CI/CD, APIs, databases, and production deployments .
• Strong understanding of software architecture, debugging, testing, scalability, and production engineering practices.

Agentic Engineering –