Engineering Manager - AI, Data & Full-Stack
Role Overview
The Engineering Manager leads a 10-person team of AI/ML, data, and full-stack engineers.
This is a management-first role focused on delivery through ambiguity, research-to-production cycles, and a changing AI landscape.
The manager partners closely with Product, Solution Architecture, and QA.
Key Skills for This Role
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Company Overview
Open Innovation AI develops solutions for managing AI workloads.
Its Open Innovation Cluster Manager orchestrates AI tasks across diverse infrastructure and supports enterprise AI scalability.
The platform is hardware agnostic and optimized for GPUs and accelerator hardware.
Role Overview
The Engineering Manager leads a 10-person team of AI/ML, data, and full-stack engineers.
This is a management-first role focused on delivery through ambiguity, research-to-production cycles, and a changing AI landscape.
The manager partners closely with Product, Solution Architecture, and QA.
Delivery and Operations
- Own quarterly and capacity planning, project staffing, and early escalation of risks, dependencies, and trade-offs.
- Unblock engineers by resolving missing context, conflict, and cross-functional decisions.
- Own incident management, postmortems, observability, and production readiness for applications and AI systems.
- Improve processes, tooling, developer experience, model evaluation, experimentation, and release workflows.
- Track engineering health metrics and address technical and operational issues.
People and Technical Leadership
- Conduct one-to-ones, career coaching, performance reviews, and hiring.
- Build a collaborative, accountable, and continuously learning engineering culture.
- Contribute to technical direction and ensure technical and model debt is addressed.
Required Experience and Qualifications
- At least four years managing engineering teams of approximately 10 people.
- Strong stakeholder management and communication of technical trade-offs to non-technical audiences.
- Technical breadth across AI, data, and application layers.
- Strong task definition and breakdown skills for research-driven and product delivery work.
- Track record of building hiring pipelines and high-accountability engineering cultures.
Bonus Points
- Hands-on familiarity with LLM application patterns including RAG, agents, and output evaluation.
- Familiarity with ML observability, data pipeline and platform design, and modern full-stack development.
- Good instinct for AI safety, data privacy, and responsible AI practices.
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