Generative AI Data Science Manager

• Architect end-to-end enterprise GenAI solutions focusing on agentic system designs, including multi-agent orchestration, autonomous task execution, tool-use chains, LLM integration, and RAG pipelines.

• Define and enforce agentic design standards across teams covering agent communication protocols, intelligent task routing, lifecycle management, shared tool registries, and orchestration framework selection (e.g., LangGraph, CrewAI, AutoGen, Semantic Kernel Agents, cloud-based frameworks).

• Align technology choices with clients preferred cloud platforms and infrastructure, ensuring compliance, cost efficiency, portability, and seamless integration with existing governance policies and services.

• Collaborate with business stakeholders to translate objectives into agentic AI requirements, defining agent capabilities, success metrics, and MVP scope for iterative delivery.

• Establish robust observability, testing, and operational standards for agentic AI solutions including telemetry, monitoring, automated regression tests, simulation environments, and production SLAs to ensure reliability.

• Lead and mentor GenAI/Agentic AI engineering teams through architecture reviews, code-level guidance, sprint planning, and quality assurance to ensure high-quality solution delivery.

• Evaluate, select, and implement agentic AI technology stacks encompassing vector databases (Pinecone, Weaviate, Azure AI Search), orchestration frameworks, observability tools (LangSmith, Langfuse, Arize AI), and AI-powered development tools, complemented by strong expertise in DevOps, LLMOps, containerized architectures, and CI/CD pipelines (Azure DevOps, GitHub Actions).

What You Must Have

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

• 8+ years of experience

• Oral and written proficiency in English required

What Sets You Apart

• Over 3 years of experience in developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost, and multi-region deployments.

• Proven expertise implementing AI interoperability protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) at scale for seamless system integration.

• Skilled in using enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.

• Advanced Python programming skills and hands-on experience with agentic AI frameworks including LangChain, LangGraph, CrewAI, and AutoGen for building robust generative AI applications.

• Deep understanding of advanced Retrieval

• Augmented Generation (RAG) architectures (Graph RAG, Vectorless RAG, Hybrid RAG) and traditional AI/ML fundamentals like model building, fine-tuning, and evaluation.

• Strong knowledge of LLM security risks—prompt injection, jailbreaking, data exfiltration, tool misuse—and experience designing defense-in-depth safeguards within agentic system architectures.

• Expertise in containerization and cloud-native orchestration (Kubernetes, Docker, serverless) and event-driven architectures for scalable deployment of agentic AI workloads; holds relevant AI or solution architecture certifications.