Technical lead building safe, evaluable LLM agents and AI infrastructure for OpenLoop’s telehealth platform. Setting architecture, observability, retrieval, and model operations direction.
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Data Scientist (Agentic engineering)
Posted via LinkedIn Recruiter (not a company profile)
Posted 2 months ago
About the role
- Air Canada seeks a Data Scientist (Agentic Engineer) to build and operate production-grade agentic AI systems, collaborating with Data Scientists on scalable cloud solutions.
Responsibilities
- As a Data Scientist (Agentic Engineer) within the Data Science & AI team at Air Canada, you will build, deploy, and operate production-grade agentic AI systems that deliver measurable business value. Working within an agentic squad, you will take designed agent workflows and transform them into scalable, reliable, and secure services across Air Canada’s cloud environments (Azure and AWS). This role focuses on system engineering, deployment, CI/CD, and runtime performance, ensuring that agentic solutions operate reliably at enterprise scale. You will collaborate closely with Data Scientists, who own agent design, to deliver end-to-end production solutions. You will join the Data Science & AI Team, a central group within Air Canada’s IT organization, supporting internal business units such as Revenue Management, Network Planning, Operations, Maintenance, Cargo, and customer-facing solutions. All initiatives follow an agile methodology, with 2-to-3-week sprints and incremental releases. Responsibilities include packaging agentic AI solutions as containerized services with well-defined APIs, deploying and operating solutions across cloud platforms, designing and maintaining CI/CD pipelines, managing runtime environments using Kubernetes and cloud-native services, integrating agentic solutions with enterprise data systems, implementing monitoring and observability pipelines, ensuring system performance, enforcing security and access control, handling enterprise stack constraints, collaborating with Data Scientists, troubleshooting issues, and contributing to platform standards.
Requirements
- 5+ years of experience in software engineering, platform engineering, or ML engineering. Strong hands-on experience with containerization (Docker) and Kubernetes. Proven experience with cloud platforms, specifically Azure and AWS. Strong experience designing and operating CI/CD pipelines. Experience building and operating production API-based services. Familiarity with LLM-based systems and agentic architectures (LangGraph a plus). Hands-on experience with observability tools (logging, tracing, monitoring). Strong programming skills in Python and backend systems. Experience with enterprise security, authentication, and integration constraints. Demonstrate punctuality and dependability to support overall team success in a fast-paced environment.
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