Lead Data Scientist-Agentic AI Engineer-Bangalore Location-Immediate

About the Role

We are looking for a highly experienced Senior AI Engineer to architect, design, and deliver enterprise-scale AI solutions leveraging Generative AI, Agentic AI, Machine Learning, and Data Science. The ideal candidate will have deep hands-on expertise combined with strong solution architecture capabilities, having led complex AI programs from ideation to production at enterprise scale.

This role demands a strategic technologist who can balance deep technical execution with architectural ownership, team leadership, and stakeholder engagement. You will be a key technical authority driving AI strategy, setting engineering standards, and building the next generation of AI platforms using Large Language Models (LLMs), AI Agents, and cloud-native technologies while mentoring AI Engineers and shaping the organization's Agentic AI roadmap.

Key Responsibilities

• Architect and lead the design of enterprise-grade Agentic AI, and Generative AI platforms addressing complex, multi-domain business problems.
• Define reference architectures for AI-powered copilots, autonomous multi-agent systems, and intelligent workflow automation using modern Agentic AI frameworks.
• Own the end-to-end architecture of Retrieval-Augmented Generation (RAG) and GraphRAG solutions, including embedding strategies, vector database design, semantic search, and enterprise knowledge integration.
• Drive the design and development of scalable, secure, and highly available AI services and APIs using Python and cloud-native architecture patterns.
• Partner with Data Science and AI Engineering teams to establish best practices for operationalizing AI models at scale.
• Set organizational standards for prompt engineering, model evaluation, fine-tuning, and optimization of LLM-based applications.
• Define enterprise AI architecture strategy, including integration patterns, scalability, security, governance, and multi-cloud/multi-model deployment approaches.
• Establish and govern MLOps and LLMOps frameworks across the organization — covering model lifecycle management, observability, monitoring, and Responsible AI practices.
• Lead architecture review boards, drive technical governance, mentor senior AI Engineers and Architects, and institutionalize engineering best practices across teams.
• Engage directly with CxO-level stakeholders and clients to shape AI strategy, define multi-year AI roadmaps, and translate business vision into scalable technical solutions.
• Champion innovation through advanced PoCs, enterprise AI accelerators, and early adoption of emerging AI research and technologies.
• Provide technical oversight across multiple concurrent AI initiatives, ensuring alignment with enterprise architecture and delivery excellence.

Required Skills & Experience

• Experience in Agentic AI, Generative AI, Data Science, and/or enterprise software engineering, with significant time spent in production deployment and technical leadership roles.
• Expert-level programming skills in Python, SQL, and enterprise API design and development.
• Deep expertise in Agentic AI, Generative AI, Deep Learning, NLP, and predictive AI models, with a track record of deploying these at enterprise scale across multiple business domains
• Strong command of Prompt Engineering, RAG/GraphRAG, embeddings, vector databases, semantic search, and rigorous AI evaluation methodologies.
• Extensive hands-on experience with Agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar — including multi-agent orchestration at scale.
• Proven experience integrating and optimizing foundation models (OpenAI, Azure OpenAI, Gemini, Claude, AWS Bedrock) within complex enterprise architectures.
• Architectural experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and enterprise CI/CD pipelines.
• Demonstrated ownership of MLOps, LLMOps, Responsible AI, AI governance frameworks, and model monitoring at an organizational level.
• Exceptional analytical, communication, stakeholder management, and mentoring skills, with experience engaging senior client and business leadership.

Leadership Expectations

• Serve as the senior technical authority for AI and Generative AI initiatives, owning architecture decisions from solution design through enterprise-scale production deployment.
• Provide strategic technical leadership and mentorship to AI Engineers, Data Scientists, Architects, and cross-functional teams across multiple projects.
• Define and enforce architecture standards, engineering best practices, and quality benchmarks across the AI engineering function.
• Lead solutioning, technical proposals, effort estimation, and innovation initiatives for large-scale AI-led engagements.
• Build and evangelize reusable AI frameworks, platform accelerators, and enterprise reference architectures to elevate delivery efficiency across the organization.
• Represent the organization's AI engineering capability in client discussions, industry forums, and internal leadership reviews.

Good to Have

• Experience with Model Context Protocol (MCP), GraphRAG, Knowledge Graphs, or multimodal AI systems at enterprise scale.
• Deep expertise with Databricks, Snowflake, Spark/PySpark, or enterprise data platforms.
• Experience designing AI observability, evaluation frameworks, and guardrails for regulated or high-compliance enterprise environments.
• Prior experience in a Principal Engineer, AI Architect, or equivalent technical leadership role.
• Published thought leadership, patents, or conference speaking engagements in AI/ML domains.
• AI/ML or Cloud certifications (Microsoft, AWS, Google Cloud, Databricks) are an added advantage.

Educational Qualification

Bachelor's/ Master's degree (preferred) in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, Statistics, or a related field.