Staff AI Operations Engineer

About the role

Arctic Wolf is scaling AI across the business, and how those systems are run and operated matters as much as the models themselves. The Staff AI Operations Engineer makes frontier AI tangible and measurable: you'll own the metrics that leaders trust, manage the technical portion of our vendor partnerships our stack depends on, and build the governance that lets us move fast. Just as important, you'll be the person who can see and provide the evidence for the end-to-end impact of our AI systems, showing how they perform on their own and together, and turning that into a narrative that communicates our story to the people who need to hear it.

Responsibilities

• Provide the Evidence for End-to-End Impact
  • Trace the end-to-end impact of our AI systems, both standalone and working in combination, connecting model behavior, pipeline performance, and downstream customer and business outcomes into a single, coherent view.
  • Turn that view into a compelling, evidence-backed narrative, pairing hard metrics with concrete examples, that communicates the value and trajectory of Arctic Wolf's AI to engineers, executives, customers, and partners.
  • Build the instrumentation and reporting that makes this story repeatable and trustworthy, so impact is something we can show on demand rather than reconstruct after the fact.
• Metrics and Measurement
  • Own the metrics and KPI framework for all AI initiatives, defining what success looks like, building dashboards that inform strategy, and ensuring quality signals feed back into continuous improvement.
  • Translate AI business value into executive reporting, building the narrative that connects operational metrics to financial and strategic outcomes.
• Optimization and Operational Excellence
  • Work with partner engineering teams on targeted optimization initiatives across AI projects to improve inference cost, latency, accuracy, and operational efficiency.
• Vendor and Partnership Strategy
  • Manage the technical side of vendor relationships and partnerships, working with the engineering teams to drive evaluation of new capabilities, monitoring performance and compliance, and keeping our AI stack competitive.
• Governance and Risk
  • Design and operationalize governance that allows AI to move fast responsibly, with review gates, compliance tracking, documentation standards, and escalation workflows.
• Communication, Influence and Mentorship
  • Influence technical direction and vendor strategy without formal authority, earning adoption through rigor and credibility.
  • Mentor engineers and operations teams, establishing a culture of operational excellence and continuous improvement across the AI organization.

Requirements

• 7+ years in data, AI/ML operations, or technical program management, shipping production systems at scale.
• 5+ years in a staff or senior-level role, with a demonstrated ability to drive organizational leverage and influence across teams without direct authority.
• Proven ability to measure and articulate the end-to-end impact of technical systems, individually and in combination, and translate it into an evidence-backed story for technical and executive audiences.
• Expertise in AI metrics and evaluation, including measurement frameworks, model evaluation best practices, and dashboards that inform decisions.
• Track record managing vendor and partnership relationships, evaluating SaaS/API-based AI tools and establishing performance accountability.
• Operational and governance acumen, including designing workflows, building compliance and audit-readiness processes, and partnering with legal and security on AI risk.
• Moderate depth in Python, SQL, and data analysis, comfortable reading code, writing analytical queries, and understanding technical constraints.
• Strong systems thinking and pragmatic judgment, plus excellent communication across technical and executive audiences.
• Experience with observability tooling (Datadog, Prometheus, CloudWatch) and GRC/governance platforms (ServiceNow, OneTrust, or similar).
• Background working with AI systems such as machine learning, generative AI, and agentic workflows.
• Cost optimization and multi-cloud deployment experience.

Qualifications

• Master's degree in Computer Science, Data Science, Engineering, or related field.
• Experience with cloud platforms (AWS, Azure, Google Cloud).
• Experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-Learn).
• Experience with data visualization tools (Tableau, Power BI).
• Experience with DevOps tools (Jenkins, GitLab CI/CD).
• Experience with Kubernetes and container orchestration.

Skills

• Data analysis and interpretation.
• Technical leadership and project management.
• Vendor management and relationship building.
• Strategic planning and execution.
• Effective communication and presentation skills.
• Problem-solving and critical thinking.

Benefits

Commensurate with experience.

Pay

$108,000 to $326,000 annually.

Schedule

Full-time.

Security Requirements

Conducts duties and responsibilities in accordance with AWN’s Information Security policies, standards, processes, and controls to protect the confidentiality, integrity and availability of AWN business information (in accordance with our employee handbook and corporate policies).