ZipRecruiter

Log In

1

Python Ml Developer Jobs in Montreal, QC (NOW HIRING)

Norbert Health

Applied AI Engineer

Montreal, QC · On-site

Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation ... Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one ...

Norbert Health

Applied AI Engineer

Montreal, QC

Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation ... Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one ...

Xsolla

AI Engineer (Infrastructure team)

Montreal, QC · On-site +1

... developer support - helping us shift from manual and reactive operations to predictive, self ... Proficiency in Python for scripting, automation, and AI/ML integration; Bash or Go a plus * Working ...

Flinks

Senior Data Scientist

Montreal, QC · Remote

Azure DevOps You don't need experience with every tool listed above - strong production-ML fundamentals matter more than direct experience with our exact stack. Python is the exception: it's a non ...

AlayaCare

Staff Data Developer

Montreal, QC · On-site

Stay current on industry trends in data engineering, ML, and cloud computing. * Provide mentorship ... Strong skills in DBT, SQL, Python, Snowflake, Iceberg, Spark, and AWS (ECS, S3, Aurora, RDS)

Flinks

Senior Data Engineer

Montreal, QC · Remote

You'll own the data and ML platform that turns models into reliable production services, harden the ... Python, SQL, Bash * Google Cloud Platform (GCP) * BigQuery and dbt * Airflow (Cloud Composer), Pub ...

next page

Showing results 1-20

All JobsPython Ml Developer JobsPython Ml Developer Jobs in Montreal, QC

Python Ml Developer information

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI and machine learning systems. While AI automation tools can handle certain tasks, MLEs are essential for creating, optimizing, and interpreting complex models, making complete replacement unlikely in the near term. MLEs need skills in programming, data analysis, and model deployment to adapt to evolving AI technologies.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in deep learning, data science, and programming with tools like Python and TensorFlow. Such roles usually involve leadership, strategic planning, and extensive experience in the field.

Which 3 jobs will survive AI?

For a Python ML Developer, roles that require complex problem-solving, creativity, and human judgment are likely to persist, such as AI research scientist, data scientist, and software engineer. These jobs involve designing, interpreting, and improving AI models, which currently require advanced expertise, critical thinking, and domain knowledge that AI cannot fully replicate. Continuous learning and staying updated with new tools and techniques are essential for long-term career resilience.

What are the key skills and qualifications needed to thrive as a Python ML Developer, and why are they important?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

Can you do ML in Python?

Yes, Python is widely used for machine learning (ML) development due to its extensive libraries such as TensorFlow, scikit-learn, and PyTorch. Python skills are essential for a Python ML developer to build, train, and deploy ML models efficiently in various environments.
What are popular job titles related to Python Ml Developer jobs in Montreal, QC? For Python Ml Developer jobs in Montreal, QC, the most frequently searched job titles are:
What job categories do people searching Python Ml Developer jobs in Montreal, QC look for? The top searched job categories for Python Ml Developer jobs in Montreal, QC are:
Python Ml Developer jobs near you
Infographic showing various Python Ml Developer job openings in Montreal, QC as of July 2026, with employment types broken down into 81% Full Time, 9% Part Time, 1% Temporary, and 9% Contract. Highlights an 76% Physical, 10% Hybrid, and 14% Remote job distribution.
Full Stack Data Science Engineering Specialist

Full Stack Data Science Engineering Specialist

Td

Montreal, QC • On-site

CA$187K - CA$261K/yr

Full-time

Re-posted 12 days ago

Job description

Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Analytics, Insights, & Artificial Intelligence

Pay Details:

$187,500 - $261,000 CADThe pay details posted reflect a temporary market premium specific to this role that is reassessed annually.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description:

Department Overview

Join a high-impact analytics team that shapes business decisions through data, insights, and AI/ML. Collaborate with business leaders and cross-functional teams to uncover opportunities, build scalable analytics solutions, and translate complex analysis into actionable insights.

Key Responsibilities

  • Lead end-to-end performance diagnostics across customer, product, and advisor dimensions to identify growth, efficiency, and primacy opportunities.
  • Translate curated data into actionable insights through hypothesis development, testing, analysis, and stakeholder storytelling.
  • Design and deliver scalable analytics assets, including datasets, dashboards, segmentation frameworks, and predictive AI/ML models.
  • Investigate, evaluate, and implement AI/ML tools and algorithms to solve complex business problems.
  • Develop compelling visualizations and data stories tailored to technical and non-technical audiences.
  • Partner with business owners to drive advanced analytics and AI/ML adoption.
  • Lead cross-functional collaboration with data scientists, engineers, IT partners, and business process owners.
  • Provide subject-matter expertise, mentorship, and guidance on advanced analytics and AI/ML methodologies.
  • Identify emerging analytical trends and data needs to improve repeatable and scalable solutions.

Required Qualifications & Skills

  • Business Acumen: Strong ability to frame and structure complex business problems in financial services / retail banking, connect analytical insights to commercial levers (growth, efficiency, customer and advisor outcomes), and translate findings into clear, actionable recommendations. Demonstrated comfort engaging with senior executives and Csuite stakeholders, influencing decisions through concise, insightdriven storytelling.
  • Applied Analytics Expertise: Demonstrated ability to creatively explore data, identify nonobvious patterns, and rigorously test hypotheses to solve complex business problems. Brings an entrepreneurial mindset to analytics by proactively identifying opportunities, challenging assumptions, and delivering highimpact insights that drive informed decisionmaking.
  • ML/AI Lifecycle Familiarity: Experience working with existing ML/AI models (adjusting inputs, interpreting outputs) and building or modifying models as needed. Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models
  • Solid cloud experience with Azure or AWS and cloud AI/ML services such as Databricks, Kubernetes, docker and container orchestration, Azure Machine Learning, Azure Data Factory
  • Visualization & Communication: Proficient in creating clear, compelling dashboards, visualizations, and data stories tailored to diverse audiences, including senior executives and Csuite leaders, translating complex analysis into concise, decisionready narratives.
  • Data Stewardship: Confident working with structured and unstructured data from multiple sources, ensuring data usability, cleanliness, and reliability. Able to build or modify data pipelines or analytical assets.
  • Core Analytical Tools: Proficient in Python, PySpark, SQL, Power BI, and Databricks (or similar platforms) for data preparation, analysis, and collaboration.
  • Strong experience with PySpark for big data processing and PyTorch for deep learning model serving.
  • Non-Technical Skills: Strong relationship management, storytelling, and business communication skills for senior audiences.

Education & Experience

  • A graduate or undergraduate degree in a quantitative or analytics-focused discipline (e.g., Business Analytics, Data Science, Statistics, Mathematics, Engineering, Computer Science, Finance, Actuarial Science).
  • 10 years of relevant experience in advanced analytics, data science, or applied AI/ML in domains such as financial services, technology, consulting, or similar industries
  • Data Manipulation: SQL, PySpark, Python
  • AI & ML: Predictive Analytics, Natural Language Processing (NLP), Supervised and Unsupervised Learning, leveraging Generative AI tools and APIs, Model Development and Deployment, Experimentation and Optimization including emerging capabilities and their application in analytical workflows.
  • Data Visualization: Power BI, Tableau
  • Cloud & Big Data Platforms: Azure (ADF, Synapse, Databricks), Snowflake
  • Data Engineering: ETL/ELT Pipelines, Apache Spark

Nice-to-Have

  • Experience in customer analytics within financial services (e.g., engagement, onboarding, cross-sell, retention, productivity insights).
  • Expertise in optimizing analytical assets (data pipelines, models, dashboards) to drive measurable business impact.
  • Bilingual proficiency (English/French).
Apercu du departement

Joignez-vous a une equipe d'analytique strategique qui soutient la prise de decision d'affaires grace a des analyses rigoureuses, aux donnees et aux capacites d'intelligence artificielle et d'apprentissage automatique (IA/AA). En partenariat etroit avec les leaders d'affaires et les equipes transversales, vous contribuerez a identifier des occasions a forte valeur ajoutee, a developper des solutions analytiques durables et a transformer des analyses complexes en recommandations claires, concretes et responsables.

Responsabilites principales
  • Diriger des analyses de performance de bout en bout couvrant les dimensions clients, produits et conseillers, afin d'identifier des occasions d'amelioration liees a la croissance, a l'efficacite operationnelle et a la relation client.
  • Convertir les donnees en informations exploitables par l'elaboration d'hypotheses, leur validation analytique et la communication structuree des constats aux parties prenantes.
  • Concevoir, developper et maintenir des actifs analytiques evolutifs, incluant des ensembles de donnees, des tableaux de bord, des cadres de segmentation et des modeles predictifs en IA/AA.
  • Evaluer et mettre en uvre des outils, techniques et algorithmes d'IA/AA afin de repondre a des enjeux d'affaires complexes, dans le respect des cadres de gouvernance et de gestion des risques.
  • Produire des visualisations et des recits de donnees clairs et percutants, adaptes a des publics techniques et non techniques.
  • Travailler en etroite collaboration avec les partenaires d'affaires afin de favoriser l'adoption de l'analytique avancee et de l'IA/AA a l'echelle de l'organisation.
  • Assurer une collaboration efficace avec les equipes de science des donnees, d'ingenierie, des TI et les responsables des processus d'affaires.
  • Agir comme expert-conseil, en offrant du mentorat et de l'accompagnement sur les methodologies avancees en analytique et en IA/AA.
  • Surveiller les tendances emergentes en analytique et les besoins en donnees afin d'ameliorer la reutilisabilite, la robustesse et l'evolutivite des solutions.
Qualifications et competences requises

Sens des affaires et communication executive

  • Capacite demontree a structurer et a resoudre des problematiques complexes dans les services financiers et les services bancaires de detail.
  • Aptitude a relier les resultats analytiques aux leviers d'affaires (croissance, efficacite, experience client et performance des conseillers) et a formuler des recommandations claires et orientees vers l'action.
  • Aisance a interagir avec des cadres superieurs et la haute direction, en influencant les decisions grace a une communication concise, factuelle et axee sur les insights.

Expertise en analytique appliquee

  • Solide experience en exploration de donnees, en identification de tendances non evidentes et en validation rigoureuse d'hypotheses afin de soutenir des decisions d'affaires eclairees.
  • Approche proactive et structuree, axee sur l'amelioration continue et la creation de valeur mesurable.

IA et apprentissage automatique

  • Experience avec des modeles existants d'IA/AA (ajustement des parametres, interpretation des resultats) ainsi qu'avec la conception ou l'evolution de modeles, au besoin.
  • Bonne connaissance de l'apprentissage automatique applique, de l'apprentissage profond et des grands modeles de langage (LLM).

Infonuagique et plateformes analytiques

  • Experience avec des environnements infonuagiques tels qu'Azure ou AWS et avec des services d'IA/AA incluant Databricks, Kubernetes, Docker, Azure Machine Learning et Azure Data Factory.

Visualisation et narration des donnees

  • Capacite a concevoir des tableaux de bord et des visualisations clairs, coherents et adaptes a divers niveaux de public, incluant la haute direction, en mettant l'accent sur la prise de decision.

Gestion et qualite des donnees

  • Aisance a travailler avec des donnees structurees et non structurees provenant de sources multiples, en assurant leur qualite, leur fiabilite et leur conformite aux normes internes.
  • Capacite a concevoir ou a ameliorer des pipelines de donnees et des actifs analytiques.

Outils analytiques

  • Maitrise de Python, PySpark, SQL, Power BI et Databricks (ou outils comparables).
  • Experience confirmee avec PySpark pour le traitement de donnees volumineuses et PyTorch pour le deploiement de modeles d'apprentissage profond.

Competences interpersonnelles

  • Excellentes habiletes en collaboration, en gestion des relations et en communication d'affaires aupres de partenaires et de dirigeants.
Formation et experience
  • Diplome universitaire (baccalaureat ou maitrise) dans un domaine quantitatif ou analytique (analytique d'affaires, science des donnees, statistique, mathematiques, genie, informatique, finance, actuariat).
  • 10 annees d'experience pertinente en analytique avancee, science des donnees ou IA/AA appliquee, idealement dans les services financiers, la technologie ou le conseil.

Competences techniques cles

  • Manipulation des donnees : SQL, PySpark, Python
  • IA et AA : analytique predictive, traitement du langage naturel (NLP), apprentissage supervise et non supervise, IA generative, developpement et deploiement de modeles, experimentation et optimisation
  • Visualisation : Power BI, Tableau
  • Infonuagique et donnees massives : Azure (ADF, Synapse, Databricks), Snowflake
  • Ingenierie des donnees : pipelines ETL/ELT, Apache Spark
Atouts
  • Experience en analytique client dans un contexte de services financiers (engagement, integration, ventes croisees, retention, productivite).
  • Capacite demontree a optimiser des actifs analytiques afin de generer des resultats d'affaires mesurables.
  • Bilinguisme (francais et anglais).

Who We Are:

TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. Together, we are reimagining what banking can be for our clients, colleagues and communities.

Our Total Rewards Package
Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well-being goals. Total Rewards at TD includes a base salary, variable compensation, and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs. Learn more

Additional Information:
We're delighted that you're considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we're committed to providing the support our colleagues need to thrive both at work and at home.

Please be advised that this job opportunity is subject to provincial regulation for employment purposes. It is imperative to acknowledge that each province or territory within the jurisdiction of Canada may have its own set of regulations, requirements.


Colleague Development

If you're interested in a specific career path or are looking to build certain skills, we want to help you succeed. You'll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.

If you're passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.

We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair op...

Apply