Senior AI Engineer in Canada Creek, Nova Scotia at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Engineer based in Canada.

As a Senior AI Engineer, you will build production-grade AI agents that help enterprises monitor, troubleshoot, and improve critical AI systems. You will own complex problems end to end, moving from ambiguous questions and research through experimentation, prototyping, and production deployment. Your work will focus on autonomous agent behavior, evaluation infrastructure, retrieval and context pipelines, and production observability. You will collaborate closely with engineering, data science, and product teams to turn emerging AI capabilities into reliable customer-facing products. The role offers significant technical ownership in a rapidly evolving category where the right solutions are still being defined. You will also help establish engineering patterns, guardrails, and reusable tooling that raise the technical standard for AI development across the organization.

• Lead AI initiatives end-to-end: Take ambiguous technical and product problems from initial research and experimentation through prototyping, validation, and production deployment, quickly identifying approaches that do not work.
• Build autonomous AI agents: Design and ship agent-powered capabilities such as root-cause analysis, incident triage, and automated monitoring, integrating them into production systems alongside engineering teams.
• Develop AI evaluation infrastructure: Create and maintain golden datasets, regression suites, offline and online scoring systems, and evaluation frameworks that enable reliable iteration on non-deterministic AI systems.
• Own retrieval and context pipelines: Build pipelines leveraging customer metadata, lineage, query history, and other relevant context while ensuring agent behavior is observable and measurable in production.
• Monitor and improve agent performance: Establish tracing, failure taxonomies, cost and latency budgets, and other operational signals to continuously improve quality, reliability, and efficiency.
• Collaborate cross-functionally: Partner with data science teams on detection quality and experiment design, and with product managers to determine what AI agents should do based on customer and business needs.
• Establish engineering standards: Define reusable patterns, guardrails, internal tooling, and best practices for building safely and effectively with LLMs and agentic systems.
• Promote AI-first engineering: Use modern AI development tools as part of everyday coding, research, experimentation, and engineering workflows.

• Production agent experience: Demonstrated experience independently building and operating autonomous AI agents in production, including systems where models use tools, make decisions, and determine subsequent actions without continuous human intervention.
• AI evaluation expertise: Proven experience designing and owning evaluation frameworks for non-deterministic AI systems, including golden datasets, regression testing, offline and online scoring, and post-launch monitoring.
• Strong Python and ML foundations: Advanced proficiency in Python, with a solid backend engineering foundation and meaningful experience in machine learning, data science, or a closely related discipline.
• Model understanding: Ability to reason about how AI and language models behave beyond simply integrating third-party APIs or frameworks.
• Problem-solving mindset: Comfortable working from ambiguous problem statements rather than detailed specifications, with the ability to design experiments, build focused prototypes, assess results, and move successful solutions into production.
• Modern AI fluency: Regular user of AI development tools such as Claude or comparable solutions for coding, research, experimentation, and engineering productivity.
• Bias toward execution: Able to prioritize shipping practical, reliable solutions over unnecessary polish while recognizing when a problem requires deeper technical investigation.
• Backend focus: Comfortable working primarily at the backend and model layers rather than frontend development.
• Communication and collaboration: Able to work effectively with engineering, data science, and product stakeholders and communicate technical trade-offs clearly.
• Nice to have: Experience with applied statistics and hypothesis testing, building or maintaining MCP servers, or working with modern data and cloud technologies such as Snowflake, Databricks, dbt, or Airflow.

• Competitive annual base salary: $180,000–$240,000, with final compensation determined by skills, experience, and location.
• Equity: Eligibility for stock options as part of the overall compensation package.
• Healthcare: Comprehensive healthcare plans.
• Retirement: 401(k) retirement plan.
• Flexible time off: Flexible paid time off designed to support sustainable working practices.
• Parental support: Paid parental leave.
• Remote-first flexibility: Work remotely across the Americas with a flexible, distributed work environment.
• Home office support: Home office stipend to help create an effective workspace.
• Connectivity reimbursement: Cell phone or Wi-Fi reimbursement.
• Wellness support: Wellness stipend to support physical and mental well-being.
• Travel: Generous travel policy.
• Inclusive culture: A diverse, collaborative environment that values different backgrounds, perspectives, beliefs, and experiences.
• High-impact work: Opportunity to build emerging AI technologies in a rapidly developing market and deliver solutions used by enterprise customers.
• Professional growth: Work alongside experienced engineering, data science, and product professionals while contributing to new technical standards and practices.