About Blend360

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.

About the Role

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.

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 building production-grade applications and services .
- Demonstrable experience building production-grade Agentic AI systems ,



beyond simple chatbots or basic RAG applications.
- Strong hands-on experience with Python and contemporary 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 .

📌 Lead AI Engineer - Agentic Engineering (Hyderabad)
🏢 Blend
📍 Hyderabad