AI Engineering Manager (Player-Coach)

Black Box•United States,

12d•Onsite

About The Position

As an Engineering Manager (Player-Coach) in Black Box’s AI Center of Excellence (CoE), you will lead and grow a team of AI engineers delivering production-grade AI solutions across enterprise platforms. The role combines people leadership with engineering execution, supporting scalable delivery as the AI portfolio grows. This is not a manager-only position: the successful candidate will review code, guide architecture decisions, and support debugging and incident response when needed, while keeping the engineering team as the primary execution engine. You will build and operate hiring and onboarding processes; coach engineers and develop technical ownership; translate portfolio priorities into squad plans; and partner with Agile Delivery and Product roles to support realistic, predictable delivery. You will provide hands-on technical leadership through design reviews, code reviews, debugging, incident response, and targeted prototyping or coding when needed. The role also establishes engineering standards and improves operational readiness, reuse, and maintainability. It drives developer productivity through standardization and automation across Azure DevOps, including PR policies, CI/CD quality gates, reusable templates, and environment setup automation. Progress is measured using agreed delivery and reliability metrics.

Requirements

At least 8 years of software engineering experience, including at least 2 years in engineering management with people management responsibilities.
Strong technical foundation and ability to review code, debug issues, and guide architecture decisions through hands-on leadership.
Proven experience leading teams delivering production software, including APIs/services, integrations, and/or user interfaces.
Demonstrated ability to hire, onboard, coach, and grow engineering talent.
Strong execution discipline and ability to translate priorities into delivery without micromanagement.
Strong engineering quality mindset covering code reviews, testing, CI/CD gates, and operational readiness.

Nice To Haves

Experience with AI/LLM product engineering, including RAG, evaluation, orchestration, or agent/tool calling.
Experience integrating solutions with enterprise platforms such as ServiceNow, SAP, Salesforce, M365/SharePoint, and Azure services.
Familiarity with security and compliance expectations, including RBAC, private networking, and approval workflows.
Experience driving developer experience initiatives, including tooling, automation, CI/CD standardization, and engineering productivity metrics.
Experience managing distributed teams and mixed staffing models, including full-time employees and contractors/vendors.
Certifications in people leadership, Agile, cloud, or AI.

Responsibilities

Build and operate a repeatable hiring pipeline, including role definitions, interview loops, candidate evaluation, and hiring decisions aligned to AI CoE priorities.
Implement structured onboarding covering access readiness, environment setup, architecture standards, and first-deliverable ramp plans.
Coach engineers through one-on-ones, feedback cycles, goal setting (OKRs), and career development plans.
Mentor tech leads and develop strong technical ownership and accountability within squads.
Manage interns and contractors and apply consistent quality standards and expectations.
Translate portfolio priorities into squad execution plans with clear ownership, sequencing, and sustainable workload distribution.
Partner with Agile Delivery and Product roles to clarify requirements, plan against realistic capacity, and maintain predictable delivery.
Drive consistent execution, reduced thrash, clear ownership of technical debt, and follow-through.
Participate in technical design reviews to ensure solutions are scalable, maintainable, and aligned with enterprise security, reliability, and integration constraints.
Review code, validate implementation approaches, and coach engineers on best practices and quality improvements.
Support critical debugging, incident response, and root cause analysis, and drive corrective and preventive actions.
Contribute to spikes, prototyping, or targeted coding when needed to unblock delivery without becoming the primary individual contributor.
Establish and enforce engineering standards for code reviews, branching, testing, and release readiness.
Improve production readiness through monitoring expectations, incident runbooks, and post-release stabilization practices.
Promote reuse and maintainability through shared libraries, reference architectures, and consistent implementation patterns.
Improve developer productivity and delivery quality through standardization and automation across Azure DevOps Repos, Boards, and Pipelines.
Define and enforce PR policies, CI/CD quality gates, reusable repository and pipeline templates, and environment setup automation.
Drive measurable improvements in throughput and reliability using agreed metrics such as lead time, deployment frequency, change failure rate, and mean time to recovery.