Enterprise AI & Data Engineering Lead

eNGINE

Pittsburgh (Allegheny County)

On-site

USD 150,000 - 190,000

Full time

32 hours ago

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Job summary

eNGINE in Pittsburgh is hiring a Manager of AI and Data Engineering to lead enterprise AI strategy, architecture, and platform delivery. This role owns the technical vision, establishes standards, and mentors engineers across Azure, Microsoft Fabric, Databricks, and Snowflake.

You will shape AI lifecycle and governance, driving scalable, secure solutions across the organization. The ideal candidate brings deep expertise in cloud-native AI, platform engineering, and emerging AI technologies, with

Qualifications

Bachelor's degree in a related discipline is required.
4+ years of hands-on data and AI engineering, including ML, analytics, or intelligent automation.
Demonstrated success delivering enterprise-scale production AI solutions.
Exceptional communication and influence skills.
Ability to lead cross-functional initiatives without direct authority.

Responsibilities

Establish technical standards, solution architectures, and engineering patterns for AI initiatives.
Lead technology decisions involving AI platforms, data platforms, cloud infrastructure, and intelligent automation.
Evaluate emerging AI technologies and determine enterprise applicability.
Create scalable architectural patterns balancing innovation, maintainability, security, and cost.
Serve as the coach for AI engineering across the organization.
Design and evolve AI platform capabilities across Azure, Microsoft Fabric, Databricks, and Snowflake.
Establish platform strategies for Generative AI, RAG, machine learning, agent frameworks, vector search, and intelligent automation.
Define standards for AI lifecycle management, model deployment, monitoring, and observability.
Build reusable engineering capabilities that accelerate AI delivery.
Establish AI engineering guardrails, governance controls, and operational standards.
Lead the design and implementation of enterprise AI agents and copilots.
Architect Retrieval-Augmented Generation (RAG) solutions leveraging enterprise knowledge assets.
Design intelligent workflows that integrate AI into operational business processes.
Build scalable API and microservices architectures exposing AI across applications.
Drive adoption of AI-powered decision support, automation, and augmentation.
Establish standards for prompt engineering, orchestration, evaluation, and AI performance measurement.
Serve as principal architect for highly complex AI and data initiatives.
Lead technical design reviews and architecture governance.
Partner with engineering teams to solve scalability, performance, reliability, and security challenges.
Mentor and manage two or more engineers.
Drive engineering excellence through best practices, standards, code quality, and technical coaching.