AI Engineering Lead - Agentic Engineering

Blend360 is an AI-first consulting and technology company helping organizations transform their businesses through data, AI, technology, and advanced analytics. Our teams build production-grade solutions that solve complex business problems and create measurable client impact.

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.

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

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

Responsibilities

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.; 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.; 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.; 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.

Requirements

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.; Claude Code / Claude Code Skills; Hermes Agent; AI coding agents or comparable agentic development platforms

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.