Lead AI Engineer

YES MADAM — AI LeadLocation: Sector 63, NoidaExperience: 12+ YearsEmployment Type: Full-TimeWork Mode: Work From OfficeTeam: Engineering / TechnologyAbout the RoleYES MADAM is looking for an experienced AI Lead to lead the architecture, development, and adoption of AI/GenAI solutions across the organization.The ideal candidate should combine strong software architecture and backend engineering expertise in Java and Python with hands-on experience in Generative AI, LLMs, RAG, AI Agents, and scalable AI systems.The role involves working closely with Engineering, Product, Data, and Technology leadership to identify high-impact AI opportunities and convert them into secure, scalable, reliable, production-ready solutions.Key ResponsibilitiesOwn the organization's AI/GenAI technology strategy and architecture.Design and lead scalable AI-powered applications and platforms.Build production-grade solutions using Python and Java.Lead development of LLM applications, RAG pipelines, AI Agents, and intelligent automation.Evaluate and integrate LLM platforms such as OpenAI, Google Gemini, AWS Bedrock, and other relevant models.Design architectures involving Vector Databases, Embeddings, Semantic Search, and RAG.Drive decisions around AI frameworks, model integration, orchestration, evaluation, and deployment.Integrate AI capabilities with existing Java/Spring Boot microservices and backend platforms.Define engineering best practices for prompt engineering, model evaluation, security, testing, observability, and responsible AI.Lead technical POCs and convert successful experiments into production-ready solutions.Identify and prioritize high-impact AI use cases with Product, Engineering, Data, and Business teams.Establish standards for code quality, scalability, reliability, security, performance, and observability.Mentor and guide AI/ML and backend engineering teams.Stay updated with GenAI, LLMs, Agentic AI, AI infrastructure, and developer tooling.Own the technical roadmap and contribute to broader technology strategy and architecture decisions.Technical Skills1. Backend & ProgrammingMust Have:Strong hands-on experience with Java and PythonStrong expertise in:JavaSpring BootREST APIsMicroservicesStrong understanding of:OOPDistributed SystemsScalable Backend ArchitectureSystem DesignExperience building high-performance, production-grade applications2. AI / Generative AIMust Have:Hands-on experience with Generative AIExperience building LLM-based applicationsStrong understanding of:LLMsRAGPrompt EngineeringLLM OrchestrationExperience building AI Agents / Agentic AI systemsExperience integrating LLM APIs and modelsExperience with:EmbeddingsSemantic SearchVector DatabasesUnderstanding of AI application evaluation, monitoring, and optimizationExperience taking AI solutions from POC → Production3. AI TechnologiesExperience with one or more:OpenAI / GPT APIsGoogle GeminiAWS BedrockLangChainLangGraphLangChain4jSpring AIPineconeQdrantMilvusWeaviateAI/ML APIsModel-serving platformsArchitecture & LeadershipThe candidate should have strong expertise in:System DesignSolution ArchitectureDistributed SystemsScalable ArchitectureTechnical LeadershipArchitecture OwnershipTechnology RoadmapsEngineering StrategyCross-functional Team LeadershipStakeholder ManagementMentoringBusiness-to-Technology TranslationProblem Solving & Decision MakingCandidate ProfileMust Have12+ years overall software engineering experienceStrong professional experience with both Java and PythonStrong hands-on AI/GenAI experienceExperience with LLMs, RAG, and AI AgentsExperience designing AI application architectureProven experience taking AI solutions from POC to ProductionStrong Java + Spring Boot + Microservices backgroundStrong System Design / Solution Architecture expertiseExperience with Cloud and Distributed SystemsTechnical leadership and architecture ownershipStrong communication and stakeholder-management skillsPreferredHigh-scale / consumer-facing technology experienceProduct company / startup experienceExperience building AI products used by large user basesStrong AWS experienceMLOps / LLMOps exposureKubernetesDockerCI/CDObservabilityAI evaluation frameworksModel monitoringDomain BonusExposure to:Recommendation SystemsPersonalizationConversational AIIntelligent AutomationKey Success MetricsThe AI Lead will be measured on:Successful delivery of production-ready AI/GenAI solutionsAdoption of AI capabilities across business and technology platformsImprovement in engineering productivity through AIScalable, secure, and reliable AI architectureSuccessful conversion of POCs into productionDevelopment and growth of the AI engineering teamMeasurable business impact from AI initiatives