Manager (AI Lead)

As an AI/ML Engineer at the mentioned company, your role involves developing and architecting cutting-edge AI solutions to solve complex business challenges. You will be responsible for designing, building, and optimizing machine learning models while ensuring reproducibility and scalability using modern MLOps practices. Key Responsibilities: - Design, build, and optimize machine learning, GenAI, and statistical models to solve complex cross-domain business challenges. - Develop reproducible, scalable workflows using modern MLOps practices. - Architect end-to-end AI systems including data pipelines, vector databases, orchestration layers, and deployment frameworks. - Explore and implement advanced LLM techniques such as embeddings, retrieval-augmented generation (RAG), and agentic workflows. - Build production-grade GenAI applications using LangChain, LangGraph, and ecosystem tools. - Develop scalable APIs to integrate ML/GenAI components with enterprise systems, SaaS products, and data platforms. - Architect and deploy AI solutions on cloud platforms like Azure, Google Cloud Platform (GCP), or AWS. - Collaborate with product teams and stakeholders to understand challenges and shape AI strategy and roadmaps. Qualifications: - 10+ years of experience in software engineering, with a focus on AI/ML. - Hands-on expertise with LangGraph, CrewAI, AutoGen, or LlamaIndex. - Deep understanding of RAG, context engineering, and long-term memory design. - Strong command of Python, vector databases, and cloud AI platforms. - Experience with evaluation frameworks to monitor agent performance and drift. - Background in leading AI pods or CoEs for internal tools. - Exceptional communication and interpersonal skills for effective collaboration with diverse teams. As an AI/ML Engineer at the mentioned company, your role involves developing and architecting cutting-edge AI solutions to solve complex business challenges. You will be responsible for designing, building, and optimizing machine learning models while ensuring reproducibility and scalability using modern MLOps practices. Key Responsibilities: - Design, build, and optimize machine learning, GenAI, and statistical models to solve complex cross-domain business challenges. - Develop reproducible, scalable workflows using modern MLOps practices. - Architect end-to-end AI systems including data pipelines, vector databases, orchestration layers, and deployment frameworks. - Explore and implement advanced LLM techniques such as embeddings, retrieval-augmented generation (RAG), and agentic workflows. - Build production-grade GenAI applications using LangChain, LangGraph, and ecosystem tools. - Develop scalable APIs to integrate ML/GenAI components with enterprise systems, SaaS products, and data platforms. - Architect and deploy AI solutions on cloud platforms like Azure, Google Cloud Platform (GCP), or AWS. - Collaborate with product teams and stakeholders to understand challenges and shape AI strategy and roadmaps. Qualifications: - 10+ years of experience in software engineering, with a focus on AI/ML. - Hands-on expertise with LangGraph, CrewAI, AutoGen, or LlamaIndex. - Deep understanding of RAG, context engineering, and long-term memory design. - Strong command of Python, vector databases, and cloud AI platforms. - Experience with evaluation frameworks to monitor agent performance and drift. - Background in leading AI pods or CoEs for internal tools. - Exceptional communication and interpersonal skills for effective collaboration with diverse teams.