Data

Machine Learning Engineer (MLOps)

Montreal, Canada

Our Challenge

As a Machine Learning Engineer, you will implement machine learning models into production by utilizing state of the art tools/algorithms and methodologies following DevOps and a test-driven development process. You will also work in close collaborations with data scientists and guide them to focus not only on model performance but also delivery stability, reproducibility and scalability of a product.

Responsibilties

  • Responsible for entire ML lifecycle (research, design, experimentation, development deployment, monitoring, and maintenance) 
  • Facilitate design and build of approaches that makes use of our suite of ML/AI tools 
  • Unlock insight for internal and external stakeholders in a coherent and compelling manner 
  • Translate complex functional and technical requirements into detailed architecture, design, and high performing software 
  • Collaborate with Data Scientists to test and scale new algorithms through pilots and productionalize the solutions at scale 
  • Support data scientists with strong Python/PySpark skills on automating and optimizing model deployments. 
  • Collaborate with Data Engineers to develop data and model pipelines 
  • Write production-strength code, bring code to production, engage in code reviews 
  • Manage, monitor, troubleshoot machine learning pipeline and infrastructure 
  • Develop common components to address pain points in machine learning project, like model lifecycle management feature store and data quality evaluation 
  • Apply the latest, cutting-edge advancements in Al/ML research to create technologies for automating business processes from end-to-end 
  • Ensure ML applications are integrated in a reliable and scalable way to bring real value to customers 
  • Work closely with the application engineering teams to integrate state-of-the-art algorithms and model research into the ML product 
  • Take full ownership of ML models, including collecting training data, deploying in production, and overseeing the quality and ongoing evaluation of models 
  • Serve as an internal expert resource and champion for architecture and deployment of data science models within the organization 
  • Coach team members on tools and techniques in the delivery of ML/Al models and analytics-based tools and analyses 

Requirements

You are:

  • Equipped with deep knowledge and proven experience with optimizing machine learning model in a production context 
  • Experienced in Python or Scala (a must!); a background in programming in C, C++, Java is beneficial. 
  • Experienced with both streaming and non-streaming analytics  
  • Experienced with SQL, Spark, Pandas, Numpy. Scipy, Statsmodels, Stan, pymc3. Caret. Scikit-learn, Keras. TensorFlow, Pytorch, Databricks is beneficial 
  • Experienced in working with large data sets, simulation/optimization and distributed computing tools (Hadoop, Hive, Spark, Gurobi, Arena, etc.) 
  • Experienced with database systems (eg.. Redshift, Athena) 
  • Equipped with database development experience using Hadoop or BigQuery and experience with a variety of relational, NoSQL, and cloud database technologies 
  • Experienced in building auto-scaling ML systems & MLOps 
  • Experienced with containerization, microservices and REST APIs 
  • Experienced in agile teams and stakeholder management in complex programs 
  • A superb team player with a professional attitude and service orientation 
  • Highly understanding of Agile Principles 
  • A strong communicator with excellent interpersonal skills, along with a strong desire to work in cross-functional teams 
  • Self-motivated with strong problem-solving and learning skills 
  • Able to build a sense of trust and rapport that creates a comfortable & effective workplace, collaborative 
  • Open minded to new approaches and learning 
  • Able to influence others 
  • Able to deploy models fast and often 
  • Equipped with a strong work ethic and ability to work at an abstract level and gain consensus 

Other information: 

  • This position offers both FTE and Contract 
  • The engagement will be On-site and Hybrid model 
  • Applications from all over Canada are accepted as long as incumbents are willing to relocate to Montreal 

We Can Offer You

  • A highly competitive compensation and benefits package 
  • A multinational organization with offices in 17 global locations and the possibility to work abroad 
  • Laptop and a mobile phone 
  • 15 days of paid annual leave 
  • Maternity & Paternity leave plans 
  • A comprehensive insurance plan including: medical, dental, vision, life insurance, and long-/short-term disability 
  • Retirement savings plans 
  • A higher education certification policy 
  • Commuter benefits 
  • Extensive training opportunities, focused on skills, substantive knowledge, and personal development 
  • On-demand Udemy for Business for all Synechron employees with free access to more than 5000 curated courses  
  • Coaching opportunities with experienced colleagues from our FinLabs and Center of Excellences (CoE) groups  
  • Cutting edge projects at the world’s leading tier-one banks, financial institutions and insurance firms 
  • A flat and approachable organization 
  • An excellent working atmosphere: regular drinks, sports activities, offsite weekends with a young, dynamic team 
  • A truly diverse, fun-loving and global work culture 

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

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