Description review
AI Engineer (Agentic Systems)
Ample Insight Inc. · Canada · back to the listing
HR standards
42/100
poor
Title ↔ description
69/100
needs work
Reads as
Machine Learning Engineer
96% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
We are looking for Data Scientists who are passionate about solving real world problems. You enjoy working with both structured and/or unstructured data and are motivated to productize scalable machine learning models. Critical thinking and problem-solving skills are essential for this role.
• Degree in Computer Science or Engineering.
• Strong Python skills and experience building production-grade AI/ML systems.
• Experience architecting complex agentic systems, including orchestration, tool use, memory, and state management.
• Strong theoretical and practical foundations in machine learning, deep learning, statistics, and optimization, with experience designing, training, evaluating, and improving ML models.
• Experience with AI evaluation, observability, reliability, and scalable infrastructure.
• Ability to quickly turn cutting-edge research into working prototypes.
• Strong problem-solving skills, product instincts, and a bias toward action.
• Bonus: Familiarity with data privacy, permission-aware AI systems, and secure AI architectures.
Love solving hard problems at the frontier of AI? Come build what's next with us.
• We have competitive compensation.
• Work on cool projects based on your interests and skills. We believe in accountability and NOT micro-management.
You will join a world-class team of engineers and data scientists from Facebook, Uber, Amazon and Google. We are a fast growing consulting firm based in Toronto with clients ranging from leading startups building impactful technologies to Fortune 500 companies looking to scale their engineering and data capabilities.
Originally posted on Himalayas
• Degree in Computer Science or Engineering.
• Strong Python skills and experience building production-grade AI/ML systems.
• Experience architecting complex agentic systems, including orchestration, tool use, memory, and state management.
• Strong theoretical and practical foundations in machine learning, deep learning, statistics, and optimization, with experience designing, training, evaluating, and improving ML models.
• Experience with AI evaluation, observability, reliability, and scalable infrastructure.
• Ability to quickly turn cutting-edge research into working prototypes.
• Strong problem-solving skills, product instincts, and a bias toward action.
• Bonus: Familiarity with data privacy, permission-aware AI systems, and secure AI architectures.
Love solving hard problems at the frontier of AI? Come build what's next with us.
• We have competitive compensation.
• Work on cool projects based on your interests and skills. We believe in accountability and NOT micro-management.
You will join a world-class team of engineers and data scientists from Facebook, Uber, Amazon and Google. We are a fast growing consulting firm based in Toronto with clients ranging from leading startups building impactful technologies to Fortune 500 companies looking to scale their engineering and data capabilities.
Originally posted on Himalayas