Lead Data Engineer - AI Engineer

Job Description Duties & Responsibilities Translate business requirements into scalable and well-documented ML pipelines and AI solutions using Databricks, Azure AI, and Snowflake. Define and drive the strategic roadmap for GenAI and agentic AI adoption across business units. Lead architecture design for multi-agent systems using modular frameworks like LangChain and Azure AI Agent Service.

Oversee development and deployment of AI agents for tasks such as customer outreach, research, and workflow automation. Establish governance frameworks for AI observability, access control, and guardrail enforcement using Unity Catalog and Azure Guardrails. Mentor and guide engineering teams on best practices in GenAI, MLOps, and agentic design patterns.

Collaborate with product, data, and engineering leadership to align AI initiatives with business goals. Evaluate emerging technologies and integrate them into the enterprise AI stack (e.g., Semantic Kernel, Foundry SDK, etc.). Requirements Basic Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.

Master’s degree or higher in Computer Science, AI, or related field. 8+ years of experience in AI/ML engineering, with 3+ years in leadership roles. Deep expertise in GenAI, agentic architectures, and enterprise AI deployment. Experience leading cross-functional teams and managing large-scale AI programs.

Strong track record in AI governance, security, and compliance. Preferred Qualifications Languages: Python, SQL, PySpark GenAI & Agentic Tools: LangChain, LangGraph, OpenAI SDK, Gemini, Azure AI Agent Service, Semantic Kernel Governance & Observability: Unity Catalog, Azure Guardrails, OpenTelemetry, Databricks AI Gateway Cloud & Data Platforms: Azure AI, Databricks, Snowflake, GCP Vertex AI Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation). Requirements Duties & Responsibilities Translate business requirements into scalable and well-documented ML pipelines and AI solutions using Databricks, Azure AI, and Snowflake.

Define and drive the strategic roadmap for GenAI and agentic AI adoption across business units. Lead architecture design for multi-agent systems using modular frameworks like LangChain and Azure AI Agent Service. Oversee development and deployment of AI agents for tasks such as customer outreach, research, and workflow automation.

Establish governance frameworks for AI observability, access control, and guardrail enforcement using Unity Catalog and Azure Guardrails. Mentor and guide engineering teams on best practices in GenAI, MLOps, and agentic design patterns. Collaborate with product, data, and engineering leadership to align AI initiatives with business goals.

Evaluate emerging technologies and integrate them into the enterprise AI stack (e.g., Semantic Kernel, Foundry SDK, etc.). Requirements Basic Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field. Master’s degree or higher in Computer Science, AI, or related field. 8+ years of experience in AI/ML engineering, with 3+ years in leadership roles.

Deep expertise in GenAI, agentic architectures, and enterprise AI deployment. Experience leading cross-functional teams and managing large-scale AI programs. Strong track record in AI governance, security, and compliance.

Preferred Qualifications Languages: Python, SQL, PySpark GenAI & Agentic Tools: LangChain, LangGraph, OpenAI SDK, Gemini, Azure AI Agent Service, Semantic Kernel Governance & Observability: Unity Catalog, Azure Guardrails, OpenTelemetry, Databricks AI Gateway Cloud & Data Platforms: Azure AI, Databricks, Snowflake, GCP Vertex AI Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation).