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.

The ideal candidate combines strong software engineering fundamentals with hands-on experience building and operating real-world Agentic AI systems.

About Blend360

• Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people.
• With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence.
• The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy.
• We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients.
• For more information, visit www.blend360.com .

Responsibilities

• You will help establish engineering practices around AI agents, context engineering, tool use, evaluations, autonomous task execution, and AI-augmented development workflows .
• 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.
• 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.
• & 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.
• 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 .

Requirements

• 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.

Preferred

• 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
• 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.
• Success Looks Like In this role, you will: Build and scale production-grade Agentic AI systems , not just prototypes or chatbots.