Senior Software Engineer - AI
New
J
JobgetherFleet technology
Fully remote position for candidates based in Canada.Full-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- DockerPostgreSQLPythonKubernetesTypeScriptCI/CDTerraform
Requirements
- Demonstrated experience as a senior software engineer, frequently shipping production software and maintaining ownership after release.
- Experience delivering AI agents or AI-powered features to production that are actively used and relied upon by users.
- Strong proficiency in Python and/or TypeScript.
- Advanced experience with agentic software development lifecycle practices, using coding agents as a primary method of building software.
- Hands-on experience with LLM APIs, MCP, and agent frameworks, including judgment about when an agent-based approach is appropriate.
- Experience designing and implementing retrieval systems, including search, RAG, and permission-aware data access.
- Familiarity with evaluation frameworks and data-driven methods for assessing AI agent production readiness and performance.
- DevOps and infrastructure experience, including Docker, Kubernetes or managed container platforms, infrastructure as code, and CI/CD pipelines.
- Experience operating production services, including observability tools such as Datadog, alerting, secrets and identity management, and incident response.
- Strong SQL skills and practical experience with PostgreSQL.
- Communicate effectively in live discussions, work independently, and be transparent about technical limitations or knowledge gaps.
- Preferred: experience or knowledge of fleet operations, transportation, or logistics.
- Preferred: experience with Databricks, including Unity Catalog or Genie; enterprise search integrations such as Confluence, SharePoint, Jira, and Slack; vector databases; identity and access control; workflow orchestration; or Azure or AWS.
Responsibilities
- Lead projects end to end, translating specifications and loosely defined business problems into requirements, implementation plans, and production launches.
- Design, build, and deploy AI agents and AI-powered features for customer-facing products and internal teams.
- Develop and extend the enterprise context layer, including data connectors, retrieval systems, permission enforcement, and MCP access.
- Build shared tooling, frameworks, and engineering practices for developing and deploying AI agents.
- Assess whether problems are best addressed with AI agents or deterministic automated workflows, and implement the appropriate solution.
- Provision and manage application infrastructure, environments, deployment pipelines, and production releases.
- Monitor and improve deployed systems, including evaluation, cost management, reliability, and failure handling.
- Use tests, continuous integration, and agent-based review practices to maintain software quality.
- Break development work into tasks coding agents can execute and engineers can verify, coordinating agents in parallel where appropriate.
- Mentor engineers and collaborate with product, support, and engineering stakeholders.
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