{"id":3779547,"url":"https://alion.io/job/iderize-lead-ai-engineer","title":"Lead AI Engineer","company":{"id":3800771,"name":"Iderize","domain":"techjays.com","url":"https://alion.io/company/iderize","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Coimbatore, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Jenkins","optional":false},{"name":"LangChain","optional":false},{"name":"Linux","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"MySQL","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"WebSockets","optional":false}],"status":"live","first_seen_at":"2026-11-04T10:57:05Z","employer_posted_date":null,"last_verified_at":"2026-11-04T10:57:05Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Lead AI Engineer\n\nExperience: 8+ Years\nLocation: Coimbatore / Hybrid\nJoining: Immediate or within 30 days\n\nAbout Techjays\n\nAt Techjays, we help businesses reimagine how they build, operate, and scale through AI. We focus on developing secure, scalable, production-grade AI solutions that solve real business problems and deliver measurable outcomes.\n\nOur engineering team brings experience from organizations such as Google, NetApp, ADP, Cognizant Consulting, and Capgemini. We combine strong engineering fundamentals with an execution-focused mindset to build advanced AI systems for global clients.\n\nAbout the Role\nWe are looking for a Lead AI Engineer to drive the architecture and development of scalable backend systems and AI-powered applications.\nIn this role, you will lead the technical design and implementation of Python-based systems, oversee LLM integrations and RAG pipelines, and develop agentic AI workflows for production environments. You will take ownership of complex engineering challenges, establish coding and architectural standards, and mentor engineers to deliver reliable, maintainable, and high-performing solutions.\n\nThis is a hands-on technical leadership role for someone who can translate complex requirements into practical engineering solutions and take them from design to production.\n\nKey Responsibilities\n1. Backend Engineering & System Architecture\nDesign and develop scalable, secure backend systems using Python and frameworks such as Django or Flask.\nArchitect RESTful APIs, WebSocket services, and distributed systems that support high-performance applications.\nApply design patterns, algorithms, and data structures to solve complex engineering challenges.\nDesign efficient database schemas, caching strategies, and data access patterns for scalable applications.\nEstablish engineering standards for code quality, testing, maintainability, and system performance.\n2. AI Engineering & LLM Integration\nArchitect and implement LLM-powered applications, retrieval-augmented generation (RAG) pipelines, and agentic AI workflows.\nDevelop solutions using vector databases, embeddings, semantic search, and prompt engineering.\nBuild and optimize multi-agent systems, orchestration workflows, and tool integrations using frameworks such as LangChain and LlamaIndex.\nImplement Model Context Protocol (MCP) integrations where appropriate to connect AI systems with external tools and data sources.\nEvaluate model fine-tuning approaches and other techniques to improve AI performance where required.\nEstablish LLMOps practices covering evaluation, observability, quality monitoring, and cost and latency optimization.\n3. Cloud Infrastructure & Deployment\nDesign and deploy cloud-native applications using AWS and GCP services.\nBuild and maintain CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, or similar tools.\nContainerize applications using Docker and support deployment and operations in Linux environments.\nImplement reliable asynchronous processing and messaging using queuing systems.\nImprove system availability, scalability, security, and deployment reliability across production environments.\n4. Technical Leadership & Engineering Excellence\nOwn technical decisions across complex backend and AI engineering initiatives.\nGuide engineers in architecture, implementation, debugging, code reviews, and engineering best practices.\nIdentify performance bottlenecks and drive improvements in application reliability and scalability.\nCollaborate with product managers, frontend engineers, data scientists, and other stakeholders to deliver effective solutions.\nEvaluate emerging AI technologies, frameworks, and tools, adopting them when they provide meaningful technical or business value.\nTake ownership of features from technical design through implementation, deployment, and production support.\nRequired Technical Skills\nBackend Development\nStrong Python programming expertise.\nExperience with Django, Flask, RESTful APIs, and WebSockets.\nStrong understanding of object-oriented programming, design patterns, algorithms, and data structures.\nDatabases & Performance\nExperience with PostgreSQL, MySQL, MongoDB, or equivalent database technologies.\nStrong understanding of relational database design, query optimization, and performance tuning.\nExperience implementing caching strategies and asynchronous processing.\nCloud & DevOps\nHands-on experience with AWS and/or GCP.\nExperience with Git, Docker, Linux, and CI/CD pipelines.\nFamiliarity with queuing systems and distributed application architectures.\nSystem Design\nAbility to design secure, scalable, and maintainable backend systems.\nExperience making architecture decisions involving performance, reliability, scalability, and operational complexity.\nPreferred AI/ML Skills\nLarge Language Models (LLMs) and prompt engineering.\nRetrieval-Augmented Generation (RAG) architecture.\nVector databases such as Pinecone, ChromaDB, or equivalent.\nEmbeddings, semantic search, and retrieval optimization.\nAgentic AI workflows and multi-agent orchestration.\nModel Context Protocol (MCP) and tool integrations.\nLangChain, LlamaIndex, or similar frameworks.\nModel fine-tuning and LLMOps practices.\nAI evaluation frameworks, observability, and production quality monitoring.\nExperience optimizing inference costs, response latency, and AI application performance.\nWhat We're Looking For\nStrong engineering fundamentals: You can solve complex technical problems and design maintainable systems.\nHands-on ownership: You take responsibility for solutions from concept through production.\nSystem-level thinking: You understand how architecture, performance, security, and scalability work together.\nPractical AI expertise: You can build and deploy AI-powered applications beyond prototypes and proof of concepts.\nTechnical leadership: You guide engineers, improve code quality, and establish sound engineering practices.\nStructured problem-solving: You make thoughtful technical decisions in ambiguous and fast-moving environments.\nClear communication: You collaborate effectively across engineering and business teams.\nContinuous improvement: You stay current with evolving AI technologies and apply them where they create value.\n\nWhat We Offer\nCompetitive compensation.\nFlexible work environment with Coimbatore office and hybrid options.\nPaid holidays and flexible time off.\nMedical insurance for self and family, with coverage of up to ₹4 lakhs per person.\nOpportunities to architect and build production-grade AI systems used by real customers.\nExposure to global clients and technically challenging projects.\nA culture that values ownership, clarity, integrity, and continuous learning.\nSkills\nPython, Retrieval Augmented Generation (RAG), Artificial Intelligence (AI), Large Language Models (LLM) tuning, Vector database, Model Context Protocol (MCP), Anthropic 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