Staff AI Engineer

Safe Software

. Design, build, evaluate, deploy, and operate end-to-end AI/ML systems in production .

Posted 9/17/2026full-timeCanadaLead💰 CA$147,000 - CA$167,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and operating end-to-end AI/ML systems, with a strong focus on model development, integration, and monitoring. Proficient in architecting agent-based systems and ensuring secure, responsible AI practices.

Highest-signal resume keywords

AI/ML System DevelopmentPython ProgrammingGenerative AI ExpertiseData Pipeline ManagementAgent-Based Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Lifecycle ManagementModel Fine-TuningMachine Learning Model EvaluationEnterprise Knowledge SystemsHybrid Search TechniquesAutomated Testing PracticesObservability in AI SystemsData Retrieval SystemsTask OrchestrationSecurity in AI Systems

Soft Skills

Technical LeadershipStakeholder CollaborationComplex Decision Explanation

Tools & Technologies

LangGraphSemantic KernelAutoGenCrewAIDatabricksSnowflakeAmazon BedrockMicrosoft FoundryAPIsVector Databases

Industry Keywords

Enterprise AI Use CasesChatbotsWorkflow AutomationIntelligent Document ProcessingRecommendation SystemsDecision SupportHybrid AI ArchitecturesMCPA2AOpen-Weight Models

Tech Stack

Tools & technologies

AzurePython

About the role

Key responsibilities & impact

Design, build, evaluate, deploy, and operate end-to-end AI/ML systems in production
Manage the full AI lifecycle, including data preparation, model development, training or fine-tuning, integration, application logic, monitoring, and continuous improvement
Architect and build agentic and multi-agent systems with orchestration, task routing, shared state, tool use, agent handoffs, human oversight, and failure recovery
Design and build enterprise knowledge and retrieval systems across structured and unstructured data
Use data pipelines, embeddings, hybrid search, reranking, metadata, and access-controlled retrieval
Integrate AI solutions with enterprise applications, APIs, databases, data platforms, and governed tools
Establish automated testing, evaluation, and observability practices
Monitor task success, model and retrieval quality, hallucination risk, tool execution, safety, latency, cost, drift, and regression
Design secure and responsible AI systems with least-privilege access, auditability, sensitive-data protection, and safeguards against prompt injection, data leakage, and unauthorized actions
Create reusable architectures, libraries, and engineering standards
Provide cross-functional technical leadership through design reviews, mentoring, and stakeholder collaboration
Evaluate emerging LLMs, frameworks, and platforms and make evidence-based adoption recommendations

Requirements

What you’ll need

8+ years across software engineering and AI/ML
3+ years building and operating AI/ML systems in production at scale
Strong software engineering skills, particularly in Python or comparable backend technologies
Experience developing, adapting, fine-tuning, evaluating, deploying, and monitoring AI or machine learning models
Applied AI experience in enterprise use cases such as chatbots, workflow automation, RAG, intelligent document processing, recommendation systems, or decision support
Strong understanding of generative AI, LLMs, retrieval systems, agent-based architectures, and applied machine learning
Experience with production data pipelines, APIs, model-serving infrastructure, testing, security, reliability, and observability
Ability to explain complex technical decisions and tradeoffs to engineering teams and business stakeholders
Experience with agent and workflow orchestration tools such as LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent
Experience with vector databases, semantic or hybrid search, embeddings, and reranking
Experience with platforms such as Databricks, Snowflake, Amazon Bedrock, Microsoft Foundry/Azure AI, or equivalent
Familiarity with MCP, A2A, self-hosted or open-weight models, and hybrid AI architectures
Legally eligible to work in Canada

Benefits

Comp & perks

Bonus
Paid time off to volunteer for Safe-organized opportunities
Annual learning budget
Training programs paid for by Safe
Flexible and remote-friendly work arrangements
3 weeks of vacation
Additional 6 paid seasonal days off per year
Extended health benefits from day 1
Dental benefits from day 1
Health or lifestyle spending benefits from day 1
Counseling benefits from day 1
Parental Leave Top-Up Program
Bi-annual profit sharing
RRSP/TFSA matching program
Complimentary parking
Bike storage