Lead Data Engineer - Agentic AI & Data Intelligence

Job Description: Lead Data Engineer – Agentic AI & Data Intelligence

Location: Onsite in Phoenix, AZ, USA

Job Summary

We are seeking an experienced Lead Data Engineer to lead the strategy, development, evaluation, and delivery of next-generation AI products focused on Agentic AI, Generative AI, and Enterprise Data Intelligence.

The ideal candidate will combine strong technical expertise with strategic product leadership to identify emerging AI technologies, define product roadmaps, evaluate AI solutions, and translate innovative capabilities into secure, scalable, and measurable enterprise products.

The role requires hands-on experience with AI agents, LLMs, RAG, enterprise search, data platforms, workflow orchestration, and AI evaluation frameworks. The candidate will collaborate closely with engineering, architecture, data science, data engineering, security, and business teams to move AI capabilities from research and proof of concept (PoC) through production and enterprise adoption.

Key Responsibilities

Define product strategy, vision, and roadmaps for Agentic AI, Generative AI, and Data Intelligence platforms.

Research emerging AI technologies, multi-agent systems, LLMs, reasoning models, tool calling, and workflow orchestration frameworks.

Identify enterprise use cases for AI-driven workflow automation, intelligent decision-making, and natural-language interaction with enterprise data.

Define technical product requirements, APIs, integration patterns, product capabilities, and success metrics.

Partner with engineering, architecture, data science, and data engineering teams to design and deliver scalable AI solutions.

Define reusable AI platform capabilities, including agent orchestration, context management, memory, tool integration, and human-in-the-loop workflows.

Evaluate third-party, open-source, and cloud-based AI solutions and drive build-versus-buy-versus-partner decisions.

Develop AI capabilities involving structured and unstructured data, RAG, semantic search, vector databases, knowledge graphs, and enterprise data retrieval.

Support hands-on PoCs and prototypes involving single-agent and multi-agent systems, REST APIs, MCP, and enterprise data integrations.

Establish evaluation frameworks and benchmarks to measure AI accuracy, task completion, reasoning quality, latency, cost, reliability, and user experience.

Define experiment hypotheses, evaluation datasets, acceptance criteria, and production-readiness requirements.

Collaborate with security, privacy, risk, and governance teams to ensure responsible AI implementation and enterprise compliance.

Prepare technical assessments, product requirement documents (PRDs), architecture recommendations, technology evaluations, and executive presentations.

Communicate product strategy, technical trade-offs, investment priorities, and measurable business outcomes to technical teams and senior leadership.

Drive AI products through the complete lifecycle, from research and experimentation to PoC, production deployment, and enterprise-scale adoption.

Required Qualifications

Significant experience in AI Product Management, Data Platform Product Management, or related technical roles.

Strong understanding of Generative AI, LLMs, Agentic AI architectures, RAG, enterprise search, and modern data platforms.

Knowledge of AI agent concepts, including planning, reasoning, tool/function calling, orchestration, memory, and context management.

Experience with enterprise data, REST APIs, JSON, Python or similar scripting languages, cloud platforms, and AI/ML services.

Experience defining technical product requirements and collaborating with engineering, architecture, data science, and data engineering teams.

Proven ability to evaluate emerging technologies through PoCs, experimentation, benchmarking, and structured technical analysis.

Experience delivering enterprise AI agents, copilots, intelligent workflow automation, or AI-powered data products.

Familiarity with agent development frameworks, orchestration tools, and interoperability standards such as Model Context Protocol (MCP).

Understanding of vector databases, embedding models, semantic layers, knowledge graphs, data catalogs, and metadata management.

Experience developing LLM and Agentic AI evaluation frameworks using automated and human evaluation methods.

Knowledge of enterprise AI governance, data security, privacy, responsible AI, identity and access management, and observability.

Excellent communication, analytical, stakeholder management, and product leadership skills.

Preferred Qualifications

Experience deploying AI agents and multi-agent systems in enterprise environments.

Familiarity with LangChain, LangGraph, Semantic Kernel, or similar agent orchestration frameworks.

Experience with cloud AI services such as Azure OpenAI, AWS Bedrock, or Google Vertex AI.

Knowledge of enterprise search, vector search, knowledge retrieval, and retrieval-augmented generation architectures.

Experience with AI benchmarking, agent observability, guardrails, and production performance monitoring.

Demonstrated ability to translate emerging AI technologies into practical business solutions and measurable outcomes.

Core Technical Skills

Agentic AI: AI Agents, Multi-Agent Systems, Tool Calling, Planning, Reasoning, Orchestration, MCP

Generative AI: LLMs, RAG, Prompt Engineering, Embeddings, AI Evaluation

Data Intelligence: Enterprise Search, Vector Databases, Knowledge Graphs, Semantic Search, Metadata, Structured and Unstructured Data

Technical Skills: REST APIs, JSON, Python, Cloud AI/ML Services, Data Platforms

Product Management: Product Strategy, Roadmaps, PRDs, PoCs, Build-vs-Buy Analysis, Benchmarking, Product Lifecycle Management

Enterprise Readiness: AI Governance, Security, Privacy, Responsible AI, Access Management, Observability

Ideal Candidate Profile

The ideal candidate is a technically strong AI Product Manager who understands both the business value and engineering requirements of Agentic AI and Data Intelligence. They should be comfortable researching new technologies, evaluating competing solutions, participating in hands-on prototypes, defining measurable product outcomes, and leading cross-functional teams to deliver secure, scalable AI products for enterprise use.