AI Engineering Manager

Job Description

AI Engineering Manager / Lead – Purple Fabric

Company: Intellect Design Arena Ltd.Business Unit: Purple Fabric (Enterprise AI Platform)Location: Chennai, IndiaExperience: 12–15 years (indicative; can be calibrated based on role seniority)Role Type: Full-timeMode of Work: Work From Office (WFO) – 5 days a week.Role OverviewWe are looking for a highly experienced AI Engineering Manager / Lead to drive the architecture, development, and delivery of enterprise-grade Generative AI and Agentic AI solutions for Purple Fabric, Intellect Design Arena’s enterprise AI platform.The ideal candidate will combine deep hands-on technical expertise in AI engineering with strong engineering leadership, capable of translating complex business requirements into scalable, secure, reliable, and production-ready AI solutions.

The role will involve building AI-powered applications, agentic workflows, retrieval-augmented generation (RAG) systems, and enterprise integrations while driving engineering excellence, innovation, and delivery outcomes.Key Responsibilities1. AI Engineering & Solution DevelopmentLead the design, development, and deployment of enterprise-grade AI applications leveraging Generative AI, LLMs, and Agentic AI architectures.Architect and implement multi-agent systems, intelligent workflows, tool calling, and autonomous task execution with appropriate human oversight.Build scalable RAG pipelines integrating enterprise data sources, knowledge repositories, vector databases, and semantic search capabilities.Develop reusable AI components, frameworks, APIs, and services to accelerate product engineering and solution delivery.Drive end-to-end AI application development, from proof of concept and experimentation to production deployment and continuous improvement.2. AI Architecture & Technical LeadershipDefine and implement scalable AI application architectures aligned with enterprise requirements for performance, reliability, security, and maintainability.Evaluate and integrate LLMs, embedding models, orchestration frameworks, vector databases, and other AI technologies based on business and technical requirements.Design robust prompt engineering, context management, agent orchestration, memory, and model interaction strategies.Establish engineering standards and best practices for AI application development, testing, deployment, and monitoring.Drive architectural decisions related to model selection, latency, token consumption, inference costs, and scalability.3. Enterprise Integration & Platform EngineeringIntegrate AI capabilities with enterprise applications, APIs, databases, knowledge systems, and existing technology ecosystems.Build secure, reusable microservices and APIs to support enterprise AI workflows.Collaborate with platform, cloud, DevOps, and security teams to establish reliable deployment and operational practices.Enable interoperability across AI models, tools, enterprise systems, and data sources.Contribute to reusable platform capabilities supporting Purple Fabric’s enterprise AI and decisioning use cases.4. AI Quality, Governance & Responsible AIEstablish evaluation frameworks to assess AI output quality, factual accuracy, groundedness, relevance, and reliability.Implement guardrails to address hallucinations, prompt injection, unsafe outputs, data leakage, and unauthorised tool execution.Drive observability and monitoring for AI applications, including tracing, latency, token usage, cost, and failure analysis.Ensure adherence to enterprise security, privacy, access control, auditability, and responsible AI practices.Implement continuous evaluation and regression testing to maintain AI solution quality as models, prompts, and data evolve.5. Engineering Management & DeliveryLead, mentor, and develop a team of AI engineers, software engineers, and other technical specialists.Own technical delivery, engineering quality, resource planning, prioritisation, and execution against agreed timelines.Conduct architecture reviews, code reviews, design discussions, and technical problem-solving sessions.Establish engineering KPIs covering delivery predictability, code quality, production stability, performance, and productivity.Work closely with Product Management, Solution Architects, Presales, and business stakeholders to translate product requirements into engineering deliverables.Foster a culture of technical ownership, experimentation, collaboration, and continuous learning.Mandatory Technical SkillsAI / GenAI: Strong hands-on experience in Generative AI, LLM-based application development, and production AI systems.Agentic AI: Experience designing agent-based applications, multi-agent orchestration, tool calling, and workflow automation.RAG: Strong understanding of document ingestion, chunking, embeddings, vector search, retrieval optimisation, reranking, and grounded response generation.Programming: Strong proficiency in Python and experience building production-grade applications, services, and APIs.LLM Frameworks: Practical experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent technologies.Model Ecosystem: Experience working with commercial or open-source LLMs, such as OpenAI, Anthropic, Gemini, or Llama-based models.Data & Integration: Experience with REST APIs, databases, data pipelines, enterprise integrations, and distributed systems.Cloud & Deployment: Experience with AWS, Azure, or GCP; containerisation and deployment using Docker and Kubernetes.AI Evaluation & Security: Experience with LLM evaluation, observability, prompt security, guardrails, and production monitoring.Engineering Practices: Strong knowledge of software architecture, CI/CD, automated testing, Git, code reviews, and production support.Preferred QualificationsExperience building enterprise AI platforms, reusable AI frameworks, or AI products at scale.Exposure to financial services, banking, payments, lending, fraud detection, risk, or other regulated enterprise environments.Experience with model gateways, model routing, inference optimisation, caching, and cost management.Familiarity with enterprise knowledge management, identity and access management, data governance, and audit requirements.Experience with MLOps, LLMOps, model lifecycle management, and AI application observability.Exposure to on-premises or private-cloud AI deployments and enterprise security constraints.Experience taking AI products from 0-to-1 development through production rollout and scaling.Leadership & CompetenciesStrong technical ownership and the ability to make sound architecture and engineering decisions.Proven ability to lead engineering teams and deliver complex technology initiatives.Excellent analytical, problem-solving, and troubleshooting skills.Ability to balance experimentation and innovation with production reliability and delivery commitments.Strong stakeholder management and communication skills.Ability to explain complex AI concepts to engineering teams, product leaders, and business stakeholders.Success MeasuresSuccessful delivery of scalable, production-ready enterprise AI capabilities.Improved reliability, quality, and performance of AI applications and agentic workflows.Reduced inference costs and latency through effective architecture and optimisation.Strong engineering quality, predictable delivery, and effective team development.Reusable AI components that accelerate product development and enterprise adoption.Compliance with enterprise security, governance, and responsible AI standards.Ideal Candidate ProfileThe ideal candidate is a hands-on AI engineering leader who has built and scaled production-grade GenAI or Agentic AI solutions, rather than someone whose experience is limited to traditional machine learning, data science, AI research, or integrating basic LLM APIs.The candidate should demonstrate the ability to architect end-to-end AI systems, lead engineering teams, solve enterprise-scale technical challenges, and translate AI capabilities into secure, reliable, business-ready products.