Senior Manager, Artificial Intelligence

Arctic Wolf

Senior Manager, AI leading development for Arctic Wolf's innovative cybersecurity technologies. Overseeing AI initiatives, collaborating across teams, and mentoring technical leaders in a high-growth environment.

Posted 7/21/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $118,700 - $280,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading technical teams to deliver high-quality AI and machine learning solutions, with a strong focus on MLOps processes, cloud-native environments, and secure coding practices. Proven ability to mentor team members and drive cross-functional initiatives to integrate AI into production effectively.

Highest-signal resume keywords

AI/ML System DeliveryMLOps Process ArchitectureGenerative AI ExpertiseCloud-Native Data ServicesTeam Leadership and Mentorship

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenerative AIMLOpsModel Training and TuningAgentic FrameworksInfrastructure-as-CodeCI/CDData EngineeringQuality MeasurementCybersecurity Concepts

Soft Skills

CollaborationMentorshipTenacityProblem-SolvingCommunication

Tools & Technologies

AWSSparkFlinkKafkaDatabricksAgentCoreLangChainLangGraph

Industry Keywords

AI RoadmapCross-Functional InitiativesSecure Coding StandardsAdversarial Behavior ModelingThreat Detection

Tech Stack

Tools & technologies

AWSCloudCyber SecurityKafkaSpark

About the role

Key responsibilities & impact

Generates and manages the day-to-day work for their team and is accountable for its outcomes.
Partners with the Product team to define and deliver the 6-month AI roadmap and contributes to longer-term planning and strategy with R&D Leadership.
Provides direction and clarity, removes obstacles, and leads teams that deliver high-quality, innovative AI solutions alongside architects, developers, data scientists, product managers, CSEs, and support people.
Set the direction for applying the right approach to each problem, from classical ML (e.g., clustering, random forests) to deep learning, fine-tuned language models, and agentic systems, in partnership with your technical peers and team members, translating complex security use cases into production-ready initiatives.
Partner with Architects and other leadership on the company’s technical roadmap, championing secure, observable, and scalable AI systems in cloud-native environments.
Oversee the design, training, deployment, and ongoing quality measurement of models and agentic experiences.
Drive continuous improvement in engineering and MLOps processes.
Uphold secure coding and acceptable-use standards across the full development life cycle.
Drive cross-functional initiatives with platform, product, and security operations teams to integrate AI into production, and see that work through to completion.
Mentor each team member and help them grow their technical and leadership skills, establishing career development plans and achievable goals.
Build collaborative relationships across teams and stakeholders.
Lead recruitment for their team and be a key contributor to hiring and recruitment strategy for both full-time and co-op roles.

Requirements

What you’ll need

Bachelor’s degree or foreign equivalent in Computer Science, Machine Learning, Data Science, or a related field, or an equivalent combination of education and experience.
Four years of experience in software, AI/ML, or data science roles (or a Master’s degree plus two years), including experience leading technical teams or projects.
Demonstrated experience leading technical teams and delivering AI/ML or Generative AI systems to production at scale.
Strong expertise in the production delivery of Generative AI or machine learning systems, including training, tuning, and ongoing quality measurement and evaluation (e.g., LLM-as-judge).
Experience with agentic frameworks (e.g., AgentCore, LangChain, LangGraph) and LLM integration.
Experience architecting MLOps processes and tooling for moving models from training to production, including drift monitoring and continuous learning in high-volume pipelines.
Familiarity with cloud-native data services (AWS preferred) and data engineering tools (e.g., Spark, Flink, Kafka, Databricks), and with infrastructure-as-code, CI/CD, and DevSecOps principles.
Exposure to cybersecurity concepts such as threat detection, MITRE ATT&CK, telemetry, and adversarial behavior modeling.
A can-do attitude, tenacity, and proven ability to work across cross-functional boundaries to drive ambiguous, high-impact initiatives to completion.

Benefits

Comp & perks

Equity for all employees
Flexible time off and paid volunteer days
RRSP and 401k match
Training and career development programs
Comprehensive private benefits plan including medical, mental health, dental, disability, life and AD&D, and value-added services
Robust Employee Assistance Program (EAP) with mental health services
Fertility support and paid parental leave