At a glanceSummarised by Seekless from the posting.
Must have14
6+ years of software engineering / AI engineering 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
Practical exposure to using AI agents as engineering tools within the SDLC, such as Claude Code / Claude Code Skills, PI, Hermes Agent, or comparable agentic development platforms
Nice to have9
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
Skills
Python
FastAPI
Docker
Kubernetes
LangGraph
LangChain
CrewAI
AutoGen
Google ADK
Claude Code
Claude Code Skills
PI
Hermes Agent
MCP
Claude
GPT
Gemini
Llama
AWS
Azure
GCP
Langfuse
Arize Phoenix
OpenTelemetry
PostgreSQL
Company Description
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.
Job Description
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.
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
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Architect and develop multi-agent and agentic systems capable of performing complex, multi-step tasks in production environments.
Build agents that integrate with APIs, databases, enterprise systems, developer tools, and other external services.
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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
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Provide technical leadership across the design and development of AI-powered software products and platforms.
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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.
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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
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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.
Qualifications
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: