Telesat (Nasdaq and TSX: TSAT) is a leading global satellite operator, providing reliable and secure satellite-delivered communications solutions worldwide to broadcast, telecommunications, corporate and government customers for over 55 years. Backed by a legacy of engineering excellence, reliability and industry-leading customer service, Telesat has grown to be one of the largest and most successful global satellite operators.
Telesat Lightspeed, our revolutionary Low Earth Orbit (LEO) satellite network, scheduled to begin service in 2027, will revolutionize global broadband connectivity for enterprise and Government users by delivering a combination of high capacity, security, resiliency and affordability with ultra-low latency and fiber-like speeds. Telesat is headquartered in Ottawa, Canada, and has offices and facilities around the world.
The company’s state-of-the-art Satellite fleet consists of 12 GEO satellites, the Canadian payload on ViaSat-1 and one LEO 3 demonstration satellite. For more information, follow Telesat on X and LinkedIn or visit www.telesat.com
We are seeking an experienced software developer with Systems Engineering domain knowledge to build practical AI-enabled tools and workflows that accelerate day-to-day Systems Engineering work for the Lightspeed program. This role has the potential to deliver outsized impact by materially improving engineering throughput, baseline quality, and decision velocity—directly supporting Lightspeed execution and creating reusable capabilities for Telesat.
This is an individual contributor role. The successful candidate is a software developer first and foremost—strong at designing, building, and shipping maintainable internal tools—who also understands (or is highly motivated to learn) classical Systems Engineering practices such as requirements quality, traceability, baselining, and change control.
The focus is not to “automate Systems Engineering,” but to deliver engineer-trusted tools and repeatable workflows that: increase velocity while preserving governance (traceability, baselining, configuration/change control); keep humans in the loop for adjudication; and produce auditable, repeatable outputs.
You will leverage modern AI (including large language models) programmatically—not just via chat—to create workflows that integrate with existing SE tools and artifacts (e.g., Jama and Confluence). In the first phase, success is defined by shipping an MVP, achieving adoption with a core SE user group, and iterating based on feedback.
If you are a strong software developer who enjoys building AI-enabled tools—and you’re motivated by making Systems Engineering teams faster without weakening rigor and governance—this may be for you.
Resource is required to work in office at Telesat HQ a minimum of 4 days per week.
- Identify and prioritize a small number of high-impact SE use cases where AI + automation reduces manual effort while preserving governance (traceability, baselining, change control).
- Identify and address gaps in the technical baseline by leveraging available artifacts in various states of completion, including CONOPS, requirements, system architecture, interface definitions, high-level design information, test cases, and other project documentation.
- Build and ship lightweight tools and automation (primarily Python) that integrate with existing SE sources (e.g., Jama and Confluence) and produce engineer-consumable outputs (reports, structured change proposals).
- Implement programmatic LLM workflows (not just interactive use), including chunking for large baselines, structured outputs, semantic search/summarization, and cross-document reasoning.
- Add guardrails so outputs are reviewable and auditable, including validation checks, acceptance criteria, and logging/provenance.
- Enable adoption by running pilots/demos, gathering feedback, iterating quickly, and documenting how to use the tools.
Later on, expand the toolset and workflows to additional baseline maintenance activities (additional checks, broader coverage, deeper integrations), as adoption and value are demonstrated.
- Improve robustness and maintainability of the tooling: packaging, testing, CI-friendly execution, and clear documentation; performance and cost controls for LLM usage (prompting strategies, caching, batching)
- Bachelor’s or Master’s in Software Engineering, Computer Science, Systems Engineering, Electrical Engineering, or related field.
- 5+ years of experience building and shipping production-quality software or internal engineering tools.
- Strong Python development skills, including data processing and text analysis. API integrations and automation, writing maintainable code (tests, packaging, documentation)
- Hands-on experience leveraging large language models (LLMs) in real projects, such as building programmatic LLM pipelines (prompting, chunking, structured outputs), implementing evaluation/validation approaches for model outputs o integrating
- LLMs into tools/workflows used by other engineers
- Working knowledge of Systems Engineering concepts and genuine interest in applying AI to SE workflows, including requirements quality criteria (clarity, atomicity, verifiability), traceability and baselining concepts, change impact analysis and configuration control principles
- Ability to translate governance needs into repeatable workflows (checks, validation gates, review steps) that scale.
- Practical experience with Jama and Confluence in a requirements-centric environment.
- Experience supporting complex systems programs (telecom, space, aerospace, defense, or similar).
- Exposure to model-based systems engineering (MBSE) and structured representations of system architectures.
- Experience building internal tools that drive adoption across a team (documentation, training, stakeholder alignment).
- In this position, the incumbent will be an individual contributor and will not have direct reports
- Based at Telesat headquarters in Ottawa, ON.
- Normal office environment with daily computer use
