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    Software Engineer - Toronto, Canada - Brunel

    Brunel
    Brunel Toronto, Canada

    4 days ago

    Default job background
    Pharmaceutical / Bio-tech
    Description

    Software Engineer, Machine Learning (6-month contract)

    Toronto, ON

    Introduction

    We are hiring a Software Engineer for Machine Learning within our clients Digital R&D Large Molecule Research Team. The Software Engineer will be responsible for leveraging large scale ML systems to augment the drug discovery process while also scaling up the company's AI solutions for the patients of tomorrow. The successful candidate should be comfortable working in large teams with disparate stakeholders where you can lead and champion technical decisions and have a keen eye for improvement opportunities and a demonstrated ability to deliver using software engineering and ML software integration skills while working across the full stack and moving fluidly between programming languages and technologies. This a 6-month contract position located in Toronto, Ontario.

    Responsibilities

    • Work in agile pods to design and build cloud hosted, ML products with automated pipelines that run, monitor, and retrain ML Models
    • Design AI/ML apps and implement automated model and pipeline adaption and validation working closely with data scientists and data engineers
    • Support the full MLOps life cycle of new and existing ML applications (e.g., new releases, change management, monitoring and troubleshooting).
    • Work as ML systems architecture design SME (e.g., develop and maintain enterprise standards, user guides, release notes, FAQs)
    • Build processes supporting seamless ML integrations (e.g., app monitoring, troubleshooting, life cycle management and customer support)
    • Maintain effective relationships with application userbase to develop education and communication content as per life cycle events
    • Research and gain expertise on emerging tools and technologies. An enthusiasm to ask questions and try and learn new things is essential

    Requirements

    • Graduate degree in Computer Science, Information Systems, Software Engineering or another quantitative field
    • Ability to work across the full stack and move fluidly between programming languages (e.g.: Python, SQL, Spark) and ML frameworks (e.g.: Airflow, MLFlow, Argo)
    • Experience in cloud and high-performance computing environments
    • Experience in AWS (e.g.: S3, Lambda, SageMaker, EKS, CloudWatch)
    • Knowledge of relational and non-relational databases
    • Experience in development, deployment and operations of AI/ML modelling of complex datasets
    • Experience in developing and maintaining APIs (e.g.: REST, gRPC)
    • Experience specifying infrastructure and Infrastructure as a code (e.g.: Docker, Kubernetes, EKS, Terraform)
    • Experience in data science, statistics, software engineering, modular design and design thinking.
    • Experience developing CI/CD pipelines for AI/ML development, deploying models to production, and managing the lifecycle in a regulated environment.
    • Experience building and deploying data science apps with large scale data and ML pipelines and architectures.
    • Experience working in an agile pod supporting and working with cross-functional teams.
    • Good understanding of ML and AI concepts and hands-on experience in development, deployment and agile life cycle management of data science apps.
    • Ability to assess new technologies and compile architecture decision records (ADRs).

    What We Offer

    Why apply through Brunel? Finding the next step in your career can be a fulltime job in itself. We manage the process for you: from submitting your resume to coordinating interviews to extending offers and assisting with on-boarding. We'll get you going while you get on with the job.

    About Us

    Brunel has a reputation for working with some of the best in the business. That's what we continually strive for. Over 45 years, we've created a global network of interesting clients and talented individuals working together through a vast array of services.

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