Lead AI Engineer - Agentic Engineering

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.