AI & Generative AI • Large Language Models (OpenAI, Claude, Gemini, Llama, Mistral) • Generative AI and Agentic AI architectures • Retrieval-Augmented Generation (RAG) • Prompt Engineering • Multi-Agent Systems • AI Orchestration Frameworks (LangChain, LangGraph, CrewAI, AutoGen) • Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Machine Learning & Data Science • Machine Learning and Deep Learning fundamentals • Model evaluation, deployment, and monitoring • Feature engineering and data pipelines Programming & Cloud • Python (mandatory) • AWS, Azure, or GCP • REST APIs and Microservices • MLOps / LLMOps • Docker and Kubernetes Architecture & Consulting • Enterprise Solution Architecture • Technical Consulting and Pre-Sales • Requirements Gathering and Solution Design • Stakeholder Management • Executive Presentations and Customer Workshops Qualifications • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. • 10–15+ years of experience in software engineering, AI/ML, data science, or solution architecture. • Proven experience delivering enterprise AI/GenAI solutions in production environments. • Strong customer-facing and leadership experience. Preferred Qualifications • Certifications in AWS, Azure, GCP, or Generative AI. • Experience with Responsible AI, AI Governance, and Security. • Exposure to Telecom, BFSI, Retail, Healthcare, Manufacturing, or Supply Chain domains. • Lead end-to-end AI solutioning activities, including discovery, architecture, design, development, and deployment. • Engage with business stakeholders, customers, and leadership teams to identify AI opportunities and define solution roadmaps. • Design and implement Generative AI, Agentic AI, and Machine Learning solutions aligned with business objectives. • Architect enterprise-grade AI applications leveraging LLMs, RAG frameworks, AI agents, vector databases, and cloud platforms. • Collaborate with pre-sales and delivery teams for solution proposals, effort estimation, and technical presentations. • Define scalable AI architecture, governance frameworks, security controls, and best practices. • Guide engineering teams through implementation, code reviews, and deployment strategies. • Drive innovation by evaluating emerging AI technologies, frameworks, and industry trends. • Mentor AI engineers, data scientists, and architects while fostering technical excellence.