Reliant AI Seeks Experienced Engineering Manager/Chef d'équipe technique

5 days ago

Montreal, Quebec, Canada Reliant AI Full time
About Reliant AI

We are a cutting-edge technology company that specializes in developing innovative machine learning solutions. Our team of experts is dedicated to creating software that can analyze complex information and provide comprehensive, factual answers. We believe that the transformative impact of generative AI will be realized by those who are willing to take on the world's biggest information challenges.

About the Role

We are seeking an experienced Engineering Manager/Chef d'équipe technique to join our team. This individual will be responsible for formulating engineering timelines, organizing work, making workflow decisions, identifying technical challenges, communicating across the organization, and helping lead the day-to-day activities of the team.

Key Responsibilities
  • Design and Architect ML Solutions: Collaborate with data scientists, engineers, and stakeholders to architect end-to-end machine learning infrastructure, including data ingestion, preprocessing, model development, deployment, and monitoring.
  • Technology Assessment and Selection: Evaluate and select appropriate ML frameworks, libraries, and tools based on project requirements, scalability, and performance.
  • Scalable and Reliable Infrastructure: Design highly scalable, distributed, and fault-tolerant model training and deployment infrastructure to handle large volumes of data and real-time inference.
  • Performance Optimization: Continuously surface opportunities to optimize the performance of machine learning models, data pipelines, and infrastructure to achieve maximum efficiency and scalability.
  • Mentoring and Knowledge Sharing: Act as a mentor to the engineering team, provide guidance, and promote a culture of knowledge sharing and continuous learning within the team.
  • Collaboration and Leadership: Collaborate with cross-functional teams and provide technical leadership. Collaborate with data scientists, engineering teams, and stakeholders to understand business requirements, provide guidance on the applicability of generative AI.
  • Ensure Data Quality and Governance: Establish processes and guidelines for data quality, validation, and governance to ensure the reliability and integrity of data used for training and inference.
  • Security and Privacy: Implement robust security and privacy measures to protect sensitive data and ensure compliance with relevant regulations.
Requirements
  • Education: Bachelor's or master's degree in computer science, engineering, or a related field. A Ph.D. in a relevant field is preferred.
  • Experience: 10+ years of experience in machine learning, data engineering, or related roles, with a focus on architecting and delivering machine learning solutions at scale.
  • Cloud Experience: Experience with cloud-based platforms (e.g., AWS, Google Cloud Platform, Azure) is required.
  • Architecture and Design Skills: Strong architectural skills with experience in designing and implementing scalable, distributed, and fault-tolerant systems. Familiarity with microservices architecture, containerization (e.g., Docker), and orchestration frameworks (e.g., Kubernetes) is a plus.
  • Programming and Tooling: Expertise in programming languages like Python, Java, or Scala, and knowledge of ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
  • Data Engineering and Preprocessing: Strong understanding of data engineering principles, data preprocessing techniques, data governance, and data integration.
  • Communication and Leadership: Excellent communication skills with the ability to communicate complex technical concepts to both technical and non-technical stakeholders. Proven leadership abilities with experience in guiding and mentoring engineering teams. Strong team player with the ability to collaborate effectively in cross-functional teams.
  • Problem-Solving and Analytical Skills: Strong analytical thinking, problem-solving, and troubleshooting skills to address complex architectural challenges.
  • Adaptability and Innovation: Ability to adapt to evolving technologies and business needs. Strong drive for innovation and continuous learning.
  • Software Engineering Skills: Familiarity with software engineering best practices, version control systems, and agile development methodologies.
  • Generative AI Experience: Experience developing machine learning applications and deploying in a business setting, especially generative AI applications, is highly desirable.

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