{"id":58528,"url":"https://alion.io/job/blend360-lead-ai-engineer-agentic-engineering","title":"Lead AI Engineer - Agentic Engineering","company":{"id":4305,"name":"Blend360","domain":"blend360.com","url":"https://alion.io/company/blend360","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":49,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":56000,"period":"year","method":"role_seniority_country_cell","sample_n":36},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Google ADK","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Arize Phoenix","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"Gemini","optional":true},{"name":"Langfuse","optional":true},{"name":"Llama","optional":true},{"name":"OpenTelemetry","optional":true}],"status":"live","first_seen_at":"2026-08-14T13:33:03Z","employer_posted_date":"2026-08-14","last_verified_at":"2026-10-16T19:20:32Z","board_verified":true,"closed_at":null,"days_open":63,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":63},"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.\n 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.\nThis 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).\nYou 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.\nThe ideal candidate combines strong software engineering fundamentals with hands-on experience building and operating real-world Agentic AI systems.\nWhat You'll Do\nAgentic Engineering & AI-Augmented SDLC\nDrive the adoption of Agentic Engineering practices across the software development lifecycle, using AI agents to augment and automate engineering workflows.\nLeverage 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.\nBuild AI-assisted workflows covering requirements analysis, code generation, code understanding, refactoring, testing, debugging, documentation, code review, and deployment.\nDesign agent workflows capable of understanding large codebases, managing context, using tools, executing multi-step engineering tasks, and recovering from failures.\nEstablish best practices around context management, context engineering, tool calling, agent orchestration, guardrails, human-in-the-loop workflows, and autonomous task execution.\nDesign and implement Evals to measure agent correctness, reliability, code quality, task completion, regression, and overall effectiveness.\nContinuously evaluate emerging agentic coding tools and techniques and identify opportunities to improve engineering productivity and software quality.\nProduction-Grade Agentic AI\nArchitect and develop multi-agent and agentic systems capable of performing complex, multi-step tasks in production environments.\nDesign agent architectures involving planning, reasoning, tool use, memory/context, execution, reflection, validation, and error recovery.\nBuild agents that integrate with APIs, databases, enterprise systems, developer tools, and other external services.\nDevelop reliable tool-use and MCP-based integrations where appropriate.\nBuild production-grade LLM applications using frameworks such as LangGraph, LangChain, or equivalent orchestration frameworks.\nImplement RAG, semantic search, vector retrieval, structured outputs, and other LLM application patterns where required.\nEstablish appropriate observability, evaluation, monitoring, security, and guardrails for agentic applications.\nSoftware Engineering & Architecture\nProvide technical leadership across the design and development of AI-powered software products and platforms.\nApply strong software engineering principles including system design, modular architecture, API design, scalability, reliability, testing, CI/CD, and maintainability.\nBuild production-quality services and APIs using technologies such as Python, FastAPI, Docker, Kubernetes, and cloud platforms.\nWork closely with engineering, product, data, and client teams to translate complex business problems into scalable technical solutions.\nConduct technical design reviews and provide mentorship to other AI/software engineers.\nEstablish engineering standards and best practices for building AI and agentic applications.\nLeadership & Innovation\nAct as a technical leader for Agentic AI initiatives and influence architecture and engineering decisions across teams.\nMentor engineers on AI engineering, agentic architectures, software engineering practices, and AI-assisted development.\nStay current with rapidly evolving AI coding agents, agent frameworks, LLM capabilities, evaluation methodologies, and engineering practices.\nPrototype emerging technologies and transition successful approaches into reliable production solutions.\nCollaborate with clients and internal stakeholders to identify opportunities where Agentic AI can deliver measurable business and engineering value.\n Must Have\n6+ years of software engineering / AI engineering experience, with strong hands-on development experience.\nStrong software engineering fundamentals with experience building production-grade applications and services.\nDemonstrable experience building production-grade Agentic AI systems, beyond simple chatbots or basic RAG applications.\nStrong understanding of Agentic Evaluation / Agent Evals, including designing evaluation frameworks for autonomous and multi-agent systems.\nExperience creating evaluation datasets, test scenarios, metrics, automated regression tests, and quality gates for agentic applications.\nAbility to evaluate agents beyond final-answer accuracy, including planning, tool use, reasoning trajectory, context handling, reliability, safety, latency, cost, and task completion.\nStrong hands-on experience with Python and modern backend/API development.\nExperience with LLMs, GenAI, agent orchestration, tool calling, and RAG.\nExperience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Google ADK, or equivalent.\nStrong understanding of multi-agent architectures, planning, reasoning, context management, tool use, memory, and agent execution.\nExperience working with Evals / evaluation frameworks to measure and improve AI/agent performance.\nExperience with cloud, containers, CI/CD, APIs, databases, and production deployments.\nStrong understanding of software architecture, debugging, testing, scalability, and production engineering practices.\nAgentic Engineering - Critical Requirement\nThe candidate should have practical exposure to using AI agents as engineering tools within the SDLC, not simply developing AI applications.\nExperience with tools such as:\nClaude Code / Claude Code Skills\nPI\nHermes Agent\nAI coding agents or comparable agentic development platforms\nis highly valuable.\nCandidates should understand how to use these tools for activities such as:\nContext management → code generation → repository understanding → implementation → testing → debugging → code review → evaluation → iteration\nNice to Have\nExperience with MCP (Model Context Protocol) and building MCP servers/tools.\nExperience with Claude, GPT, Gemini, Llama, or other frontier models.\nExperience with AWS, Azure, or GCP.\nExperience with Kubernetes, Docker, CI/CD, and cloud-native architectures.\nExperience with LLM observability and tracing.\nExperience with tools such as Langfuse, Arize Phoenix, OpenTelemetry, or similar.\nExperience implementing automated agent evaluations, regression testing, and quality gates.\nExperience with distributed systems and scalable AI inference.\nExperience working in consulting/client-facing environments.\n What Success Looks Like\nIn this role, you will:\nBuild and scale production-grade Agentic AI systems, not just prototypes or chatbots.\nHelp Blend360 adopt Agentic Engineering across the SDLC.\nImprove developer productivity through AI-assisted engineering workflows.\nEstablish repeatable approaches for context engineering, agent orchestration, tool use, and Evals.\nHelp teams safely adopt AI coding agents such as Claude Code, PI, Hermes Agent, and emerging equivalents.\nRaise the engineering quality, reliability, and scalability of AI solutions delivered to clients.\nMentor engineers and become a technical authority in Agentic AI Engineering.","description_format":"text","description_chars":8360,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Recruiting & Staffing","AI Consulting & Integration","Analytics & BI Consulting","Data Engineering & Migration Services"],"lifecycle":[{"event":"open","at":"2026-08-14T13:33:03Z"}],"visa":[],"liveness":{"score":8,"band":"cold","label":"Long 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