Staff AI Developer

At Arctic Wolf, you won’t just watch the cybersecurity industry evolve – you'll help lead the change. Our global Pack is made up of people who thrive on solving hard problems, moving fast, and building technology that protects organizations around the world. We’re proud to be recognized by Forbes, CNBC, Fortune, CRN, Bartner Peer Insights and IDC MarketScape – but what matters most is the work behind it: delivering real outcomes for customers through award winning innovation like our Aurora Platform.
If you’re looking for meaningful work, smart teammates and the chance to make a real impact in a high-growth company that’s redefining security operations, Arctic Wolf is the right place for youOur mission is simple: End Cyber Risk. We’re looking for a Staff AI Developer to be part of making that happen.

About the Role
The Staff AI Developer builds and delivers the AI features that power Arctic Wolf Labs, working across machine learning, data science, generative AI, and agentic systems. This is a hands-on senior engineering role: you'll take significant features and components from design through production, and you'll be one of the people the team relies on to ship well.
You don't need to be a specialist in every AI discipline, but you should be comfortable working with the data science team on data science concepts, delivery mechanisms, and trade-offs across ML, generative AI, and agentic systems, enough to make sound design decisions and collaborate credibly with specialists.
ResponsibilitiesDesign & DeliveryDesign and build production-grade AI systems and components across ML pipelines, generative AI, and agentic workflows.
Turn ambiguous problems into working systems that unify heterogeneous data sources using rule-based, probabilistic, and ML-based approaches.
Apply best practices for secure, observable, and scalable AI systems in cloud-native environments (AWS preferred).
Help evaluate emerging frameworks and approaches (agentic orchestration, fine-tuning, evaluation methods) and bring recommendations to the team.
Build and maintain ML and data pipelines for training, deploying, and monitoring fine-tuned generative AI and machine learning models.
Cross-Functional PartnershipWork with the data science team, engineering managers, product, and security operations analysts to deliver solutions that create measurable customer value.
Translate between disciplines, helping data scientists understand production constraints and helping partners understand what the AI solution needs.
Collaborate with security operations, threat researchers, and product teams to ground your work in real operational workflows and quality feedback loops.
CommunicationCommunicate progress, trade-offs, and technical decisions clearly, in writing and verbally, to both technical and non-technical audiences.
Write clear technical documents: design docs, architecture decision records, and status updates the team can act on.
Hands-On Engineering & Mentorshi pOwn the day-to-day engineering: write code, review PRs, debug production issues, and unblock teammates.
Mentor mid-level and early-career engineers, and raise the bar on code quality, testing, and operational hygiene.
Instrument AI quality, measure outcomes, and help close the feedback loop with operations teams.
About You6+ years building intelligent systems, distributed platforms, or AI-enhanced applications.
Experience designing and shipping systems that support ML, data science, generative AI, or agentic workloads, without necessarily being a deep practitioner in all of them.
Production experience with generative AI systems (prompt engineering, evaluation, guardrails, and some exposure to fine-tuning).
Experience with agentic frameworks and LLM integration patterns (e.g., AgentCore, Amazon Bedrock, LangGraph, or equivalent).
Track record of partnering with cross-functional teams (data science, product, operations) to deliver customer-facing outcomes.
Strong written and verbal communication skills across technical and non-technical audiences.
Strong hands-on coding ability, you spend most of your time building.
Experience with cloud-native data services (AWS preferred) and infrastructure-as-code / CI/CD practices.
Exposure to cybersecurity concepts: threat detection, alert triage, risk modelling, exposure management, MITRE ATT&CK, telemetry.
Familiarity with data engineering tools (Spark, Flink, Kafka, Databricks).
Experience with ML quality measurement and evaluation frameworks (LLM-as-judge, human-in-the-loop evaluation).
DevSecOps and security-first development mindset.
On-Camera PolicyTo support a fair, transparent, and engaging interview experience, candidates interviewing remotely are expected to be on camera during all video interviews. Being on camera fosters authentic connection, improves communication, and allows for full engagement from both candidates and interviewers. We understand that technical, bandwidth, or location-related challenges may occasionally prevent video use. If this appli