IT Consulting professional

Job brief:
An ideal candidate will have 7 to 12 years of experience working in live projects related to Artificial Intelligence, Responsibilities: Lead the design and implementation of enterprise-grade Machine Learning, Deep Learning and Generative AI solutions, Define and execute the AI technology projects, aligning Generative AI, Machine Learning, and Deep Learning initiatives with business strategy and data architecture, Design, evaluate, and guide development of autonomous and agentic AI systems capable of reasoning, planning, and tool-based decision-making, Architect end-to-end RAG pipelines including document processing, embedding generation, vector storage, retrieval mechanisms, and contextual response systems, Oversee Machine Learning and Deep Learning model development, training, deployment, and optimization for predictive and generative use cases, Establish robust LLMOps / MLOps pipelines ensuring model lifecycle management, observability, version control, and performance monitoring, Collaborate with data engineering, analytics, and DevOps teams to integrate AI systems with enterprise data warehouse/lakes, APIs, and reporting environments, work with stakeholders to identify, evaluate, and prioritize AI-driven business opportunities, focusing on measurable impact and innovation, Provide technical leadership, mentorship, and guidance to AI engineers, data scientists, and developers, fostering a high-performance innovation culture, Stay ahead of emerging AI research, frameworks, and tools, driving continuous evolution of enterprise AI capabilities, Skills Areas: Deep expertise in Generative AI, Large Language Models (GPT, Claude, Llama, Mistral, etc,), and RAG architectures, Strong proficiency in Agentic AI concepts ? autonomous agents, multi-agent orchestration, and reasoning loops, Advanced understanding of Machine Learning (ML) and Deep Learning (DL) algorithms, architectures (Transformers, CNNs, RNNs), and model deployment workflows, Experience architecting solutions with AWS agentic AI framework, LangChain, LlamaIndex, Semantic Kernel, or similar frameworks, Hands-on experience with RDBMS, No SQL and vector databases Proficiency in Python, scikit-learn, PyTorch, TensorFlow, and Hugging Face Transformers, Expertise in LLMOps / MLOps practices model training, CI/CD, observability, and drift management, Deep understanding of cloud-native AI architecture (AWS Sagemaker, Azure AI, GCP Vertex AI), Strong foundation in AI governance, ethical AI, and compliance frameworks, Excellent leadership, communication, and stakeholder management skills to align AI initiatives with business goals, Good-to-Have Skills: Experience in Data Engineering ETL/ELT pipeline design, data modeling, data warehousing, and integration with AI pipelines, Familiarity with Data Visualization and BI tools such as Power BI, Tableau, Amazon QuickSight especially for building analytical insights from AI-driven data, Understanding of DataOps and analytics architectures supporting AI and decision intelligence systems, Knowledge of enterprise data platforms and API-based data integration to connect AI models with structured and unstructured data sources, Experience: The ideal candidate will have relevant experience of > 7 years Possession of a professional degree / post-graduation is desirable Certifications in the respective technology areas will be an added advantage,