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 – Critical Requirement
• The candidate should have practical exposure to  using AI agents as engineering tools within the SDLC , not simply developing AI applications.

Experience with tools such as

• Claude Code / Claude Code Skills
• PI
• Hermes Agent
• AI coding agents or comparable agentic development platforms

is highly valuable.

Candidates should understand how to use these tools for activities such as

Context management → code generation → repository understanding → implementation → testing → debugging → code review → evaluation → iteration

Nice to Have

• Experience with  MCP (Model Context Protocol)  and building MCP servers/tools.
• Experience with  Claude, GPT, Gemini, Llama, or other frontier models .
• Experience with  AWS, Azure, or GCP .
• Experience with  Kubernetes, Docker, CI/CD, and cloud-native architectures .
• Experience with  LLM observability and tracing .
• Experience with tools such as  Langfuse, Arize Phoenix, OpenTelemetry, or similar .
• Experience implementing  automated agent evaluations, regression testing, and quality gates .
• Experience with distributed systems and scalable AI inference.
• Experience working in consulting/client-facing environments.

What Success Looks Like

In this role, you will

• Build and scale  production-grade Agentic AI systems , not just prototypes or chatbots.
• Help Blend360 adopt  Agentic Engineering across the SDLC .
• Improve developer productivity through AI-assisted engineering workflows.
• Establish repeatable approaches for  context engineering, agent orchestration, tool use, and Evals .
• Help teams safely adopt AI coding agents such as  Claude Code, PI, Hermes Agent, and emerging equivalents .
• Raise the engineering quality, reliability, and scalability of AI solutions delivered to clients.
• Mentor engineers and become a technical authority in  Agentic AI Engineering .