Senior Manager, Ai Software Engineering

📍 United States

USD 200,000-275,000 per year

SENIOR

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 AWS @ 3 CI/CD @ 3 Communication @ 6 Distributed Systems @ 7 Docker @ 3 Engineering Management @ 7 GCP @ 3 Hiring @ 6 Kubernetes @ 3 LLM @ 4 MLOps @ 3 Machine Learning @ 4 Observability @ 7 Security @ 7

Details

Responsibilities

Lead and grow one or more AI Engineering teams focused on delivering production AI capabilities and platform services.
Drive execution for strategic AI initiatives across the P&T organization, including customer-facing features, internal AI platform capabilities, and supporting engineering systems.
Partner with Product Management and cross-functional engineering leaders to translate roadmap priorities into clear technical plans, delivery milestones, and team outcomes.
Establish strong engineering management practices across planning, execution, quality, incident response, observability, and continuous improvement.
Mentor engineering managers and senior individual contributors while building a culture of accountability, innovation, and operational excellence.
Help define and enforce best practices for AI system development, MLOps, service reliability, security, and performance.
Support architecture and design decisions for scalable, low-latency, highly available AI systems and services.
Recruit, develop, and retain top engineering talent across AI, software, and platform domains.
Improve collaboration across globally distributed teams and ensure effective delivery across organizational boundaries.
Contribute to organization design, capacity planning, and quarterly and annual planning aligned to P&T and AI goals.
Operate as a delivery-focused engineering leader who balances innovation with reliability; raise the bar on execution discipline, technical quality, and cross-functional collaboration; build healthy teams with clear accountability, strong coaching, and high trust; make pragmatic decisions, surface risks early, and drive issues to resolution; and foster a culture that values customer impact, learning velocity, and engineering excellence.

Requirements

8 or more years of experience in software engineering, machine learning engineering, or closely related technical fields.
3 or more years of experience leading engineering teams, including managing managers and/or senior engineers in a high-growth environment.
Proven track record of shipping production-grade software, AI, or data products at scale.
Strong technical depth in distributed systems, cloud-native services, and modern software engineering practices.
Experience building or operating AI and ML systems, including model integration, evaluation, deployment, monitoring, and lifecycle management.
Familiarity with MLOps and platform capabilities required to support reliable AI product delivery.
Strong understanding of engineering quality, security, observability, and operational excellence practices.
Experience partnering effectively with Product, Design, Security, Data, and Infrastructure teams.
Excellent communication skills and the ability to align senior stakeholders around priorities, tradeoffs, and execution plans.
Demonstrated success hiring and developing high-performing engineering teams.
Experience leading teams that build AI-powered product features in cybersecurity, enterprise software, or data-intensive environments is preferred.
Experience with large-scale event processing, real-time systems, or detection and analytics platforms is preferred.
Hands-on familiarity with cloud platforms such as AWS or GCP, as well as Kubernetes, Docker, and modern CI/CD practices is preferred.
Experience with LLM-powered applications, agentic workflows, retrieval systems, or applied AI platform services is preferred.
Background in endpoint security, cloud security, SIEM, or adjacent security domains is preferred.