Staff AI Developer

Arctic Wolf

. Design and build production-grade AI systems and components across ML pipelines, generative AI, and agentic workflows .

Posted 10/8/2026full-timeRemote • CanadaLead💰 CA$75,000 - CA$246,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and building production-grade AI systems, with a strong focus on ML pipelines, generative AI, and agentic workflows. Proficient in collaborating across disciplines to deliver measurable customer value while ensuring secure and scalable cloud-native environments.

Highest-signal resume keywords

Generative AI SystemsMachine Learning PipelinesCloud-Native Data ServicesAgentic FrameworksStrong Communication Skills

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningGenerative AIPrompt EngineeringEvaluation FrameworksCodingML Quality MeasurementInfrastructure-as-CodeCI/CD PracticesData ScienceAgentic Workloads

Soft Skills

CollaborationMentoringTechnical CommunicationProblem-SolvingAdaptability

Tools & Technologies

AWSSparkFlinkKafkaDatabricksAgentCoreAmazon BedrockLangGraphDevSecOpsTelemetry

Industry Keywords

CybersecurityThreat DetectionRisk ModellingMITRE ATT&CKOperational Workflows

Tech Stack

Tools & technologies

AWSCloudCyber SecurityKafkaSpark

About the role

Key responsibilities & impact

Design 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
Evaluate emerging frameworks and approaches, including agentic orchestration, fine-tuning, and 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
Work with data science, engineering managers, product, and security operations analysts to deliver measurable customer value
Translate between disciplines by explaining production constraints to data scientists and AI requirements to partners
Collaborate with security operations, threat researchers, and product teams to ground work in operational workflows and feedback loops
Communicate progress, trade-offs, and technical decisions to technical and non-technical audiences
Write design documents, architecture decision records, and status updates
Write code, review pull requests, debug production issues, and unblock teammates
Mentor mid-level and early-career engineers and raise standards for code quality, testing, and operational hygiene
Instrument AI quality, measure outcomes, and help close feedback loops with operations teams

Requirements

What you’ll need

6+ 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
Production experience with generative AI systems, including prompt engineering, evaluation, guardrails, and some exposure to fine-tuning
Experience with agentic frameworks and LLM integration patterns, such as AgentCore, Amazon Bedrock, LangGraph, or equivalent
Track record of partnering with data science, product, and operations teams to deliver customer-facing outcomes
Strong written and verbal communication skills across technical and non-technical audiences
Strong hands-on coding ability
Experience with cloud-native data services, AWS preferred, and infrastructure-as-code / CI/CD practices
Exposure to cybersecurity concepts including threat detection, alert triage, risk modelling, exposure management, MITRE ATT&CK, and telemetry
Familiarity with Spark, Flink, Kafka, and Databricks
Experience with ML quality measurement and evaluation frameworks, including LLM-as-judge and human-in-the-loop evaluation
DevSecOps and security-first development mindset
Background checks are required for this position
Candidates interviewing remotely are expected to be on camera during all video interviews, subject to accommodations

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
Variable incentive compensation
New hire equity grants