AI Engineering Lead - Agentic Engineering

Blend360; Hyderabad; On-site; INR 3,600,000 - 6,000,000; Full time

Job summary

Blend360 is seeking an experienced AI Engineering Lead to shape and build Agentic AI and AI-powered engineering systems. This role focuses on production-grade agentic technology, SDLC integration, and scalable AI workflows.

You will lead AI/engineering initiatives, implement agent architectures, and mentor teams across AI, software, and client engagements, delivering measurable business impact.

Qualifications

• 6+ years of software/AI engineering experience.
• Experience building production-grade AI systems beyond chatbots.
• Strong understanding of Agentic Evaluation / Agent Evals.
• Experience creating evaluation datasets, test scenarios and quality gates.
• Proficiency in Python and modern backend/API development.
• Experience with LLMs, agent orchestration, tool calling, and RAG.
• Experience with cloud, containers, CI/CD, APIs, databases, and production deployments.

Responsibilities

• Architect and lead production-grade AI systems within the SDLC.
• Drive Agentic Engineering practices across the software lifecycle.
• Design agent workflows for large codebases, context management, tool use, and autonomous task execution.
• Mentor engineers in AI engineering, agentic architectures, and development practices.
• Collaborate with clients and internal teams to deliver measurable AI/engineering value.

Skills

Python

Production-grade AI

LLMs/GenAI

Agentic Eng.

SDLC integration

AI tooling

Tools

LangGraph

LangChain

Claude Code

Hermes Agent

Docker

Kubernetes

Job description

AI Engineering Lead - Agentic Engineering

• Full-time

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 ofAgentic 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 buildproduction-grade agentic systems and, importantly, how to leverageAgentic 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 aroundAI 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 ofAgentic Engineering practices across the software development lifecycle, using AI agents to augment and automate engineering workflows.
• Leverage tools and approaches such asClaude 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 coveringrequirements 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 aroundcontext management, context engineering, tool calling, agent orchestration, guardrails, human-in-the-loop workflows, and autonomous task execution.
• Design and implementEvalsto 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 developmulti-agent and agentic systemscapable of performing complex, multi-step tasks in production environments.
• Design agent architectures involvingplanning, 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 reliabletool-use and MCP-based integrationswhere appropriate.
• Build production-grade LLM applications using frameworks such asLangGraph, LangChain, or equivalent orchestration frameworks.
• Implement RAG, semantic search, vector retrieval, structured outputs, and other LLM application patterns where required.
• Establish appropriateobservability, evaluation, monitoring, security, and guardrailsfor 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 includingsystem design, modular architecture, API design, scalability, reliability, testing, CI/CD, and maintainability.
• Build production-quality services and APIs using technologies such asPython, 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 onAI 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.

What We're Looking For

Must Have

• 6+ years of software engineering / AI engineering experience, with strong hands-on development experience.
• Strong software engineering fundamentals with experience buildingproduction-grade applications and services.
• Demonstrable experience buildingproduction-grade Agentic AI systems, beyond simple chatbots or basic RAG applications.
• Strong understanding ofAgentic Evaluation / Agent Evals, including designing evaluation frameworks for autonomous and multi-agent systems.
• Experience creatingevaluation datasets, test scenarios, metrics, automated regression tests, and quality gatesfor agentic applications.
• Ability to evaluate agents beyond final-answer accuracy, includingplanning, tool use, reasoning trajectory, context handling, reliability, safety, latency, cost, and task completion.
• Strong hands-on experience withPythonand modern backend/API development.
• Experience withLLMs, GenAI, agent orchestration, tool calling, and RAG.
• Experience with agent frameworks such asLangGraph, LangChain, CrewAI, AutoGen, Google ADK, or equivalent.
• Strong understanding ofmulti-agent architectures, planning, reasoning, context management, tool use, memory, and agent execution.
• Experience working withEvals / evaluation frameworksto measure and improve AI/agent performance.
• Experience withcloud, 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 tousing AI agents as engineering tools within the SDLC, not simply developing AI applications.

Experience with tools such as:

• 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:

Nice to Have

• Experience withMCP (Model Context Protocol)and building MCP servers/tools.
• Experience withClaude, GPT, Gemini, Llama, or other frontier models.
• Experience withAWS, Azure, or GCP.
• Experience withKubernetes, Docker, CI/CD, and cloud-native architectures.
• Experience withLLM observability and tracing.
• Experience with tools such asLangfuse, Arize Phoenix, OpenTelemetry, or similar.
• Experience implementingautomated 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 scaleproduction-grade Agentic AI systems, not just prototypes or chatbots.
• Help Blend360 adoptAgentic Engineering across the SDLC.
• Improve developer productivity through AI-assisted engineering workflows.
• Establish repeatable approaches forcontext engineering, agent orchestration, tool use, and Evals.
• Help teams safely adopt AI coding agents such asClaude 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 inAgentic AI Engineering.

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