In this role, you will design, build, and optimize robust ML models, data pipelines, and intelligent agentic solutions. You will be responsible for the full lifecycle of model deployment, transforming experimental code into production-ready software by applying rigid development best practices (TDD, CI/CD). Collaborating cross-functionally with data scientists, architects, and product teams, you will establish automated MLOps infrastructure—including monitoring, validation, and drift detection—to ensure system reliability in an AWS environment.
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Advantages
Cutting-Edge AI Scope: Direct involvement in building and optimizing both traditional ML models and next-generation Agentic solutions with a high degree of autonomy.
End-to-End Technical Ownership: Lead the operational deployment (MLOps) of AI models, directly influencing infrastructure automation, model tracking, and system performance.
Cross-Functional Collaboration: Serve as the technical software anchor within a diverse ecosystem of data scientists, enterprise architects, and product managers.
Modern Cloud Stack: Deepen your expertise in cloud-native deployment using AWS, enterprise data pipelines, and advanced platforms like Databricks.
Responsibilities
1. ML Engineering & MLOps Architecture
Build, optimize, and scale machine learning models and end-to-end data pipelines.
Design and implement critical operational aspects of model deployment, including automation pipelines, continuous monitoring, and automated drift detection.
Transition experimental models and prototypes into robust, maintainable, and production-grade software applications.
Participate in research experiments and rapid prototyping to validate next-generation AI concepts.
2. Software Development & Quality Assurance
Provide core software engineering expertise to internal data analytics and data science delivery teams.
Apply strict software development best practices, including Test-Driven Development (TDD) and automated CI/CD workflows.
Review requirements, map system dependencies, and provide accurate implementation effort estimations during team planning sessions.
Test, debug, and optimize application code to eliminate performance bottlenecks.
Conduct thorough code reviews and provide constructive feedback to elevate overall team code quality.
3. Cross-Functional Synergy
Collaborate closely with architects, data scientists, product teams, and business stakeholders to translate high-level goals into functional ML architectures.
Actively contribute to team planning, daily standups, and retrospective sprint cycles.
Qualifications
Core Requirements
Education: BSc. or MSc. degree in Computer Science, Engineering, Mathematics, Physics, Statistics, or an equivalent quantitative discipline.
AI/ML Experience: Minimum of 3+ years of professional experience successfully delivering AI/ML projects.
Software Experience: Minimum of 2+ years operating explicitly as a software developer within a structured delivery team.
Autonomy: Mastery of ML algorithms, techniques, and Agentic frameworks, with the proven ability to optimize models with minimal supervision.
Technical Skillset
Polyglot Programming: Advanced expertise in at least two common development languages (e.g., Python, Java, C#).
Data Ecosystem: Proficient working knowledge of general Python data packages and relational/non-relational databases and query engines (e.g., SQL).
Cloud & DevOps: Strong foundational knowledge of DevOps automation practices and building production solutions within AWS.
Statistical Aptitude: Proficient with statistical concepts and capable of applying rigorous statistical thinking to solve complex business problems.
Preferred Qualifications (Nice to Have)
Hands-on experience building scale-ready data engineering pipelines.
Familiarity or working exposure to modern web frontend development architectures.
Deep technical familiarity with enterprise data platforms like Databricks.
Advanced mastery of the broader AWS service ecosystem.
Summary
Our Client is seeking a highly skilled Machine Learning Software Engineer to bridge the gap between data science prototypes and production-grade AI solutions. This role is fundamentally a software development position with a heavy specialization in Machine Learning, MLOps, and Agentic AI. You will provide core software engineering expertise to data science teams, ensuring that advanced algorithms are scalable, maintainable, and seamlessly integrated into the enterprise cloud ecosystem.
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
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In this role, you will design, build, and optimize robust ML models, data pipelines, and intelligent agentic solutions. You will be responsible for the full lifecycle of model deployment, transforming experimental code into production-ready software by applying rigid development best practices (TDD, CI/CD). Collaborating cross-functionally with data scientists, architects, and product teams, you will establish automated MLOps infrastructure—including monitoring, validation, and drift detection—to ensure system reliability in an AWS environment.
Advantages
Cutting-Edge AI Scope: Direct involvement in building and optimizing both traditional ML models and next-generation Agentic solutions with a high degree of autonomy.
End-to-End Technical Ownership: Lead the operational deployment (MLOps) of AI models, directly influencing infrastructure automation, model tracking, and system performance.
Cross-Functional Collaboration: Serve as the technical software anchor within a diverse ecosystem of data scientists, enterprise architects, and product managers.
Modern Cloud Stack: Deepen your expertise in cloud-native deployment using AWS, enterprise data pipelines, and advanced platforms like Databricks.
...
Responsibilities
1. ML Engineering & MLOps Architecture
Build, optimize, and scale machine learning models and end-to-end data pipelines.
Design and implement critical operational aspects of model deployment, including automation pipelines, continuous monitoring, and automated drift detection.
Transition experimental models and prototypes into robust, maintainable, and production-grade software applications.
Participate in research experiments and rapid prototyping to validate next-generation AI concepts.
2. Software Development & Quality Assurance
Provide core software engineering expertise to internal data analytics and data science delivery teams.
Apply strict software development best practices, including Test-Driven Development (TDD) and automated CI/CD workflows.
Review requirements, map system dependencies, and provide accurate implementation effort estimations during team planning sessions.
Test, debug, and optimize application code to eliminate performance bottlenecks.
Conduct thorough code reviews and provide constructive feedback to elevate overall team code quality.
3. Cross-Functional Synergy
Collaborate closely with architects, data scientists, product teams, and business stakeholders to translate high-level goals into functional ML architectures.
Actively contribute to team planning, daily standups, and retrospective sprint cycles.
Qualifications
Core Requirements
Education: BSc. or MSc. degree in Computer Science, Engineering, Mathematics, Physics, Statistics, or an equivalent quantitative discipline.
AI/ML Experience: Minimum of 3+ years of professional experience successfully delivering AI/ML projects.
Software Experience: Minimum of 2+ years operating explicitly as a software developer within a structured delivery team.
Autonomy: Mastery of ML algorithms, techniques, and Agentic frameworks, with the proven ability to optimize models with minimal supervision.
Technical Skillset
Polyglot Programming: Advanced expertise in at least two common development languages (e.g., Python, Java, C#).
Data Ecosystem: Proficient working knowledge of general Python data packages and relational/non-relational databases and query engines (e.g., SQL).
Cloud & DevOps: Strong foundational knowledge of DevOps automation practices and building production solutions within AWS.
Statistical Aptitude: Proficient with statistical concepts and capable of applying rigorous statistical thinking to solve complex business problems.
Preferred Qualifications (Nice to Have)
Hands-on experience building scale-ready data engineering pipelines.
Familiarity or working exposure to modern web frontend development architectures.
Deep technical familiarity with enterprise data platforms like Databricks.
Advanced mastery of the broader AWS service ecosystem.
Summary
Our Client is seeking a highly skilled Machine Learning Software Engineer to bridge the gap between data science prototypes and production-grade AI solutions. This role is fundamentally a software development position with a heavy specialization in Machine Learning, MLOps, and Agentic AI. You will provide core software engineering expertise to data science teams, ensuring that advanced algorithms are scalable, maintainable, and seamlessly integrated into the enterprise cloud ecosystem.
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
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