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Sr. AI​/ML Engineer

Job in Longueuil, Province de Québec, Canada
Listing for: Alert Media, Inc.
Full Time position
Listed on 2026-10-11
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 110000 - 170000 CAD Yearly CAD 110000.00 170000.00 YEAR
Job Description & How to Apply Below
Position: Sr. AI/ML Engineer )

At Alert Media , we help organizations protect their people, operations, and brand. Our modern Risk Intelligence and Response platform empowers teams to detect emerging threats, assess impact, and respond with confidence. We believe building resilience should be simpler—and it starts with bringing critical information and workflows together in one unified platform.

Our core values drive us in our important mission of keeping people safe & informed:

  • We’re humans not robots
  • Customers always come first
  • We work better together
  • Simplicity is our strength
  • Our reputation is priceless
  • Hard work pays off

Alert Media is looking for an innovative and hands-on Sr. AI/ML Engineer with a strong background in applied machine learning and data science to lead the evaluation, training, and integration of advanced AI and machine learning models across our product suite. In this role, you will help shape our AI strategy by identifying the most appropriate models for our use cases, optimizing their performance through fine-tuning and prompt engineering, and ensuring their responsible deployment within our platforms.

You will have the opportunity to show who you are throughout our interview process; we don’t ask any trick questions or expect you to perform a whiteboard exercise. We want to see you shine by showing us work you are most proud of!

Who you are:

You are an applied AI and machine learning engineer with strong data science fundamentals, deeply curious about what's possible with large language models and practical machine learning applications. You’re a collaborative engineer who thrives in a fast-moving environment, and you’re as comfortable training and fine-tuning models as you are crafting prompts or evaluating model outputs. You have a strong understanding of ML fundamentals, but you're pragmatic in applying the right solution for the problem.

What

you get to do every day:
  • Train and fine-tune supervised learning models (e.g., classification, ranking, embeddings) for internal tools and customer-facing product features.
  • Lead prompt engineering and testing efforts to fine-tune LLM behavior for specific use cases across the product suite.
  • Design and implement evaluation pipelines for both LLMs and traditional ML models.
  • Work with and optimize Elasticsearch-backed data systems that power AI and intelligence pipelines.
  • Proactively identify opportunities to improve AI models, data pipelines, and engineering workflows beyond the existing roadmap.
  • Develop tools and frameworks to support prompt iteration, model benchmarking, and performance tracking.
  • Coordinate with security team to ensure adherence to AI compliance standards.
  • Evaluate and compare commercial and open-source LLMs (e.g., OpenAI, Anthropic, Mistral, Meta, etc.) for performance, cost, alignment, and reliability.
  • Collaborate with product and engineering teams to integrate AI features in a safe, scalable, and maintainable way.
  • Monitor, evaluate, and continuously improve the performance of AI and machine learning models in production.
  • Stay informed of emerging research in NLP, machine learning, and AI infrastructure, and evaluate potential applications for the business.
  • Contribute to Alert Media’s culture, values and vision for the future.
What you bring to the role:
  • Bachelor’s degree in a technical field such as computer science, computer engineering or related field preferred.
  • 2+ years of software engineering experience, Python preferred.
  • 4–6+ years of experience in data science and/or software engineering, specifically in machine learning engineering, or applied AI.
  • Strong understanding of LLMs and traditional machine learning methods.
  • Experience with prompt engineering, model fine-tuning, and evaluation.
  • Familiarity with model evaluation best practices, including…
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