Director of AI Platform Engineering & Performance

Koitecc Solutions; Charlotte, Northern (NC, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Bank of America seeks a distinguished leader to define and drive architecture and standards for agentic AI platform engineering, improving latency, scalability, and cost efficiency across the SDLC. You will lead teams delivering reliable AI capabilities embedded in developer workflows.

The role emphasizes hands-on depth in runtime performance, model monitoring, and SRE practices, with a track record of scalable, observable systems and strong collaboration with executives and engineering partners.

Qualifications

• 15+ years of engineering leadership across enterprise platforms or AI-enabled systems.
• Proven ability to define architecture, standards, and technical strategy in regulated environments.
• Experience leading performance, reliability, and platform scalability initiatives.

Responsibilities

• Manages relationships with executives, sponsors, and vendors for technical products and outcomes.
• Fosters inclusive, efficient environments and removes blockers to improve delivery.
• Oversees portfolio resourcing, budgets, and alignment to strategic technology outcomes.
• Leads performance reviews, coaching, and development plans for teams.
• Defines architecture, standards, and governance across the SDLC for AI platforms.

Skills

Executive leadership

Analytical thinking

Collaboration

Business acumen

Influence

Result orientation

Risk management

Data management

Stakeholder management

Technical strategy development

Education

Advanced degree in a technical discipline

Bachelor's degree in a technical field

Tools

DevOps tooling

SRE tooling

Job description

Bank of America seeks a distinguished leader to define and drive architecture and standards for agentic AI platform engineering, improving latency, scalability, and cost efficiency across the SDLC. You will lead teams delivering reliable AI capabilities embedded in developer workflows.

The role emphasizes hands-on depth in runtime performance, model monitoring, and SRE practices, with a track record of scalable, observable systems and strong collaboration with executives and engineering partners.