Principal AI/ML Engineer

Svitla Systems Inc. is looking for a PRINCIPAL AI/ML ENGINEER for a full-time position (40 hours per week) in Canada and USA. Our client develops advanced software solutions that help organizations collect, analyze, and manage complex digital data. Its technology supports teams working with large volumes of information, enabling them to uncover insights, streamline workflows, and make informed decisions. The platform combines data processing, analytics, automation, and secure collaboration capabilities to address complex investigative and security-related challenges. The company serves a global customer base across both public and private sectors.

You will join our client’s team, building and operating AI systems that power our client’s digital forensics capabilities. The work ranges from model training and evaluation through deployment, retrieval-augmented generation (RAG) pipelines, and agentic workflows. You will own complex initiatives end to end, shipping AI-powered systems that surface critical leads and insights for investigators, helping them solve cases faster and with greater confidence. You will work closely with Product, UX, and other engineering teams to ensure the models and systems advance what’s possible while meeting real-world constraints.

The work on our client’s team is diverse. In a given quarter, you might design an evaluation harness for an agentic system, fine-tune a model, prototype a retrieval pipeline, or partner with other engineering teams on production scaling. You will work across the full stack (training, evaluation, RAG, agentic systems, deployment, and scale), and you will be trusted to pick the right tool for each problem. The team works on challenging and meaningful problems, from building AI that investigators can trust to turn months of case work into days, to figuring out what “good” looks like when users are searching for a needle in a haystack they have never seen before. If these are the kinds of problems that interest you, you will feel at home on this team.

Requirements

• 5+ years of professional experience in ML or applied AI (or equivalent depth demonstrated through delivered work), with a track record of delivering models or AI systems into production.
• 10+ years of software engineering experience, with significant leadership as a senior individual contributor.
• Demonstrated depth in at least one of the following: model training and evaluation, agentic system design, or retrieval and evaluation architecture, with working familiarity across the other areas.
• Experience evaluating ML/AI systems: designing representative evaluation distributions and checking that training signals or metrics reflect the actual outcome that matters.
• Comfort working with large, complex, and/or unstructured datasets, with a strong understanding of trade-offs between model quality, cost, inference speed, and system complexity.
• Proficiency in Python and working fluency with modern ML/AI frameworks and tooling (such as PyTorch, inference servers, and LLM/agent frameworks).
• Strong communication and cross-functional collaboration skills, including working with engineers, researchers, and the Product and Design teams.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.

Will be a plus

• Familiarity with vector databases, embedding models, and context retrieval strategies.
• Background in NLP, computer vision, or other relevant ML domains.
• Familiarity with MLOps tooling (such as experiment tracking, model versioning, and CI/CD for ML).
• Contributions to open-source ML/AI projects or publications in peer-reviewed venues.
• Experience working with cloud providers, such as AWS or Azure, or with other relevant production AI/ML infrastructure.
• Experience working with AI tools as part of the development workflow (such as Claude and GitHub Copilot).

Responsibilities

• Own complex AI/ML initiatives from ideation through experimentation, evaluation, deployment, and handoff for integration.
• Design and prototype agentic workflows where models reason, plan, call tools, and collaborate with other systems to accomplish complex tasks.
• Train, fine-tune, or adapt models when the problem demands it, and know when a well-designed system beats a bigger model.
• Work with complex, real-world datasets, developing pre-processing, augmentation, and evaluation techniques that enhance model quality and fairness.
• Collaborate cross-functionally with other engineering teams to ensure models and systems are production-ready, observable, and scalable, and meet real user needs.
• Contribute to reusable engineering infrastructure that accelerates experimentation, evaluation, and deployment.
• Embed ethical, responsible, and secure AI practices into design, evaluation, and deployment decisions, raising concerns early when they surface.
• Mentor engineers at different levels on experimental design, evaluation methodology, and technical decision-making.
• Help the team level up by establishing patterns and best practices.