Lead D

About the Role

Lead-level AI engineer to design and deliver enterprise-scale Agentic and Generative AI platforms using LLMs, multi-agent orchestration, RAG/GraphRAG and vector search. Own end-to-end architecture, MLOps/LLMOps, cloud-native deployments, mentor AI teams, and engage with senior stakeholders to translate business needs into scalable AI solutions.

Heavy focus on agentic AI and LLM-based platforms—building AI dev tools, multi-agent systems, and RAG/vector search workflows.

Location: Bengaluru

Type: Full Time, Permanent

Posted: 2 days ago

Category: AI/ML & Data

Vibe Markers: 💻 Open Source, 🔥 Hot

Experience Level: Staff/Principal

Job Description

Role

Lead architecture, design, and delivery of enterprise-scale Agentic AI and Generative AI solutions. Serve as the senior technical authority for AI initiatives, owning solution and reference architectures, setting engineering standards, and guiding multi-year AI roadmaps.

Key Responsibilities

• Architect and lead design of enterprise-grade Agentic AI, Generative AI platforms, and AI-powered copilots.
• Define reference architectures for autonomous multi-agent systems, retrieval-augmented generation (RAG/GraphRAG), embeddings, vector databases, and semantic search.
• Own end-to-end architecture for RAG/GraphRAG solutions and enterprise knowledge integration.
• Design and develop scalable, secure, highly available AI services and APIs using Python and cloud-native patterns.
• Establish and govern MLOps and LLMOps frameworks, including model lifecycle management, observability, monitoring, and Responsible AI practices.
• Set organizational standards for prompt engineering, model evaluation, fine-tuning, and optimization of LLM-based applications.
• Lead architecture review boards, technical governance, and mentor senior AI Engineers and Architects.
• Engage with CxO-level stakeholders and clients to shape AI strategy and translate business vision into technical solutions.
• Drive PoCs, enterprise accelerators, and early adoption of emerging AI technologies.

Requirements

• Significant experience in Agentic AI, Generative AI, Data Science, and enterprise software engineering with production deployments and technical leadership.
• Expert-level programming skills in Python and SQL; strong enterprise API design and development experience.
• Deep expertise in Deep Learning, NLP, prompt engineering, RAG/GraphRAG, embeddings, vector databases, and semantic search.
• Hands-on experience with Agentic AI frameworks (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen) and multi-agent orchestration.
• Experience integrating and optimizing foundation models (OpenAI, Azure OpenAI, Gemini, Claude, AWS Bedrock) in enterprise architectures.
• Architectural experience with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and enterprise CI/CD pipelines.
• Demonstrated ownership of MLOps/LLMOps, Responsible AI, AI governance, model monitoring, observability, and evaluation frameworks.
• Strong analytical, communication, stakeholder management, mentoring, and leadership skills.

Good to Have

• Experience with Model Context Protocol (MCP), Knowledge Graphs, multimodal AI, Databricks, Snowflake, Spark/PySpark.
• Prior Principal Engineer / AI Architect experience, published thought leadership, patents, conference talks, or relevant cloud/ML certifications.

Education

• Bachelor’s or Master’s degree preferred in Computer Science, AI, Data Science, Engineering, Mathematics, Statistics, or related field.

Tech Stack

Python, SQL, LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, OpenAI, Azure OpenAI, Gemini, Claude, AWS Bedrock, AWS, Azure, GCP, Docker, Kubernetes, CI/CD, Databricks, Snowflake, Spark, PySpark, Vector databases, Retrieval-Augmented Generation (RAG), GraphRAG, Knowledge Graphs, Model Context Protocol (MCP)

Skills

Solution Architecture, AI Strategy, Team Leadership, Stakeholder Engagement, Mentoring, Technical Governance, Prompt Engineering, Model Evaluation, MLOps, LLMOps, Observability and Monitoring, Responsible AI / AI Governance, API Design, Scalability and Security, Cloud Architecture, Analytical Thinking, Communication, Project Estimation