Generative ai Architect - Lead Data Scientist

Responsibilities

• Lead evaluation, benchmarking, and implementation of scalable, secure, and compliant Agentic AI frameworks aligned with enterprise business use cases and organizational strategies.

• Drive technical standardization and maturity in Generative and Agentic AI development by creating reusable orchestration blueprints, prompt engineering guides, best practices, and integration patterns to ensure consistent delivery in complex programs.

• Define scalable multi-agent system design patterns including tool usage, memory management, task routing, agent communication, human-in-the-loop processes, and orchestration frameworks.

• Establish evaluation frameworks to assess Agentic AI solution performance, focusing on reasoning, tool reliability, task completion, hallucination mitigation, and autonomous decision quality for production readiness and continuous improvement.

• Design enterprise Retrieval Augmented Generation (RAG) architectures optimizing retrieval processes, embeddings, chunking, and reranking aligned with business goals and data governance, while mentoring AI engineering teams and collaborating with stakeholders to translate objectives into AI roadmaps.

• Manage client engagements including discovery, demos, proposal development, and delivery planning, leveraging strong DevOps, LLMOps, containerization, and CI/CD pipeline expertise to ensure successful, trusted partnerships and stay updated on Agentic AI and Generative AI advancements.

What You Must Have

• Bachelor’s & Master’s Degree in computer science, Data Science, or a related field

• 12+ years of experience in technical/technology roles

• Oral and written proficiency in English required

What Sets You Apart

• Proven experience with enterprise cloud AI platforms like Azure AI Foundry (with Microsoft Fabric and Purview), Amazon Bedrock (with Knowledge Bases and Guardrails), and Google Vertex AI (with BigQuery and Agentspace) for building, governing, and deploying scalable production-grade Gen AI and Agentic AI solutions.

• Hands-on expertise with leading agentic and AI orchestration frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalents for AI workflow automation and orchestration.

• In-depth knowledge of vector databases (Pinecone, Weaviate, Milvus, Azure AI Search) and their application in enterprise Retrieval Augmented Generation (RAG) and knowledge retrieval systems.

• Experience designing and scaling AI solutions that utilize multi-modal capabilities across text, vision, audio, and document understanding to enhance AI functionality.

• Familiarity with AI governance and observability platforms (e.g., LangSmith, Arize) to enable monitoring, tracing, debugging, and compliance of production AI systems, along with expertise in selecting appropriate LLMs (GPT, Claude, Gemini, etc.) based on enterprise cost, latency, and compliance requirements.

• Knowledge of AI-powered development tools (GitHub Copilot, Cursor, Windsurf) to improve engineering productivity and facilitate large-scale AI-driven software development