Remote Director of AI Engineering & Innovation

IntePros

Philadelphia (Philadelphia County)

Remote

USD 180,000 - 240,000

Full time

14 days+

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Job summary

IntePros is seeking a Director of AI Engineering in Philadelphia. This remote role with occasional office visits requires leading enterprise AI architecture, governance, and delivery across business units.

You will drive AI strategy, vendor collaboration, and secure, scalable solutions with strong stakeholder partnership. You will oversee MLOps/LLMOps practices, ensure regulatory compliance, and mentor teams to build a mature AI engineering capability aligned with client needs and continuous

Qualifications

Bachelor's degree in Computer Science, Engineering, IT, Data Science, AI or related field.
10+ years of progressive software/AI engineering experience including enterprise initiatives.
5+ years of leadership managing technical teams or engineering functions.
Experience as senior technical authority for AI initiatives: evaluate use cases, architectures, governance, vendor oversight.
Experience with Generative AI, LLMs, RAG, agentic AI systems, and enterprise AI platforms.
Strong cloud-native architectures, API design, security, and governance in regulated environments.
Experience with external vendors/partners and translating strategy into scalable solutions.

Responsibilities

Serve as the client's senior technical leader for AI engineering, providing architectural direction and guidance.
Partner with stakeholders to evaluate AI opportunities and determine approaches, architectures, and implementation strategies.
Define AI engineering standards, reference architectures, and best practices for scalable, secure solutions.
Lead architecture, design, implementation, and operationalization of AI solutions across use cases.
Establish patterns for generative AI, RAG, agentic workflows, and AI-enabled automation.
Design AI integrations across platforms and data environments; drive lifecycle from ideation to production.
Maintain AI engineering, MLOps, and LLMOps practices across dev, test, and production.
Monitor costs, forecast resources, and establish metrics for AI adoption and value realization.
Collaborate with Cybersecurity, Infrastructure, Quality, and Data teams on controls and governance.
Mentor teams; build AI engineering capability and foster innovation across the function.