Engineering Manager/Chef d’équipe technique

at  Reliant AI

ABOUT US:

We believe that making the best decisions means looking at all the facts – a near-impossible task in our era of information overload. To fix this, we are building the next generation of machine learning software. Powered by generative AI, our algorithms analyze key information sources and provide comprehensive, factual answers for even your most complex queries.
We believe that the transformative impact of generative AI will be only realized by those willing to take on the world’s biggest information challenges. To make this future come true, we deploy our longstanding expertise in reinforcement learning and natural language processing.
We are scientists. Builders. Entrepreneurs. We spearheaded many of AI’s most impactful applications. We led teams at Google, DeepMind, and EY Parthenon. We now bridge cutting-edge AI research and the biopharma industry.

ABOUT THE ROLE:

This team member will work with us to formulate engineering timelines, organize work, make workflow decisions (e.g. coding style), identify technical challenges, communicate across the organization, and help lead the day to day activities of the team.
The ideal candidate has worked at the intersection of machine learning and engineering, understands the key technical challenges in working with ML systems, and has led teams of engineers to deliver successful software solutions.
We are looking for someone who can be hands-on on the short term and establish themselves as both an engineering expert and leader, and then progressively take on a larger amount of responsibility.

WHAT YOU’LL DO:

• 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.