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Pythian

Cloud migration and managed analytics services

Lead AI/Machine Learning Engineer

Applied Machine LearningPosted on Nov 5
Full-timeRemote in CanadaCA$140k - CA$160k /yr

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Engineering, Artificial Intelligence, or a related quantitative field is required; a Ph.D. is a plus.
  • Seven to ten years of progressive experience in machine learning engineering, software development with an ML/AI focus, or a related role is required.
  • Expert knowledge of ADK and other agentic frameworks, with deep experience implementing agents in production, is required.
  • Technical leadership experience, including mentoring engineers, driving architectural choices, and managing project delivery, is required.
  • Expert programming skills in Python and proficiency in ML/AI frameworks such as TensorFlow, PyTorch, or Scikit-learn are required.
  • Hands-on experience deploying and fine-tuning advanced pre-trained models, such as Large Language Models or similar Generative AI technologies, in low-latency production environments is required.
  • Extensive experience with cloud platforms (AWS, GCP, Azure) and container orchestration using Docker and Kubernetes is required.
  • Proficiency in data engineering principles, ETL/ELT processes, and version control systems such as Git is required.
  • Experience orchestrating complex machine learning pipelines using open-source tools such as Kubeflow and MLflow or managed cloud services is required.
  • Experience building and optimizing scalable, distributed AI/ML systems is required.
  • Deep familiarity with MLOps best practices, including model monitoring, logging, explainability, and CI/CD pipelines for AI assets, is required.
  • Strong communication and teamwork skills, and the ability to work effectively across cross-functional teams including solution architects, data scientists, and business stakeholders, are required.
  • The successful applicant must fulfill the requirements necessary to obtain a background check.

Responsibilities

  • Lead the architectural design and strategic development of high-performance AI and machine learning pipelines for complex enterprise initiatives.
  • Provide technical leadership, mentorship, and guidance to AI/ML engineers and data scientists, fostering technical excellence and career growth.
  • Deploy, manage, and scale diverse AI models, including pre-trained models such as LLMs and vision models, and custom ML models, into production environments.
  • Work with data scientists and researchers to operationalize models, transforming prototypes into resilient, production-grade AI systems that meet stringent business requirements.
  • Optimize model performance, latency, and cost-efficiency across cloud and edge deployment targets.
  • Integrate AI/ML solutions with major cloud platforms (AWS, GCP, Azure) and use Docker and Kubernetes for consistent deployment.
  • Implement MLOps practices, including automated testing, CI/CD for models, model versioning, monitoring, and proactive maintenance systems.
  • Manage stakeholders and partner with senior leadership, product managers, and clients to align technical AI roadmaps with business strategy.
  • Work with software engineering teams to ensure seamless and ethical integration of AI capabilities into larger applications and user workflows.
  • Stay current with advancements in AI/ML research, Generative AI, data engineering, and MLOps deployment strategies.

Desired Qualifications

  • A Ph.D. is a plus.

About the Company

Pythian

Cloud migration and managed analytics services

Pythian helps organizations move, manage, and analyze data and IT infrastructure. It provides cloud migration, managed services, and advanced analytics to help businesses use cloud platforms such as Google Cloud, AWS, and Azure. Its solutions include moving data and applications to the cloud, running and monitoring those systems, and turning data into insights through machine learning, artificial intelligence, and data science. Pythian differentiates itself by offering a fully managed analytics platform that unifies a company’s data across sources and by combining expert consulting with ongoing operations to ensure data systems run smoothly. The company’s goal is to enable enterprises to improve decision-making, reduce IT friction, and drive growth by leveraging cloud technology and data analytics.

Company size
501-1,000
Company stage
Early VC
Total funding
$21M
Headquarters
Ottawa, Canada
Founded
1997

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