MLOps Team Lead

📋 Description

• • As an MLOps Team Lead at Fundamental, you will be at the forefront of revolutionizing enterprise decision-making through cutting-edge AI. Fundamental is a pioneering AI company, founded by alumni from DeepMind, that has developed NEXUS, the world's most powerful Large Tabular Model (LTM). NEXUS is specifically engineered for the structured data that is critical to enterprise operations, unlocking trillions of dollars in value by empowering businesses with the 'Power to Predict'. Backed by prominent investors and trusted by Fortune 100 companies, Fundamental offers a unique opportunity to work on unprecedented technical challenges in foundation model development and build technology that fundamentally transforms how the world's largest corporations operate.
• • This role is an exceptional chance to be an integral part of a category-defining company from its inception. You will join a team that is actively shaping the future of enterprise AI. Your primary responsibility will be to lead and mentor a high-performing team of MLOps engineers. This leadership extends to fostering their technical growth, nurturing a collaborative environment, and instilling a culture of operational excellence across all MLOps initiatives. You will be instrumental in guiding the team to achieve ambitious goals and deliver impactful solutions.
• • A core aspect of your role will involve defining and driving the MLOps roadmap. This strategic planning must ensure that our infrastructure capabilities are tightly aligned with the objectives of our Research, Engineering, and Product teams. You will be responsible for translating business needs and research breakthroughs into robust, scalable, and efficient MLOps solutions. This requires a deep understanding of the entire ML lifecycle, from experimentation and development to deployment and ongoing monitoring.
• • You will establish and champion best practices, standards, and processes for our ML infrastructure, deployment strategies, and operational procedures. This includes setting the bar for code quality, testing, security, and reliability. Your technical leadership will be crucial in ensuring that our MLOps practices are scalable, maintainable, and aligned with industry-leading standards. This proactive approach will minimize risks and maximize the efficiency of our ML operations.
• • Owning technical decision-making for ML infrastructure architecture and tooling choices is a key responsibility. You will evaluate, select, and implement the most effective tools and technologies to support our growing needs. This involves staying abreast of the rapidly evolving MLOps landscape and making informed decisions that balance innovation with stability and cost-effectiveness. Your architectural vision will shape the foundation of our ML platform.
• • You will architect and oversee the implementation of scalable, automated machine learning pipelines. This includes designing and managing robust CI/CD workflows tailored for ML, ensuring seamless integration of code, data, and models. You will also be responsible for orchestration frameworks that manage complex ML workflows, ensuring reliability and efficiency.
• • Driving the design and implementation of robust model serving infrastructure is paramount. You will leverage platforms such as Triton, TorchServe, TensorFlow Serving, and KServe to deploy models efficiently. A significant part of this will be defining an inference architecture strategy that is optimized for ultra-low latency and high throughput, critical for real-time enterprise applications.
• • Designing and maintaining feature stores, robust data pipelines, and scalable storage solutions is essential for efficiently handling the large volumes of data required for both model training and inference. This involves ensuring data quality, accessibility, and performance.
• • You will collaborate closely with our research teams to effectively bridge the gap between experimental breakthroughs and production-ready systems. This requires excellent communication skills to translate complex research concepts into actionable engineering requirements and to provide feedback from production environments back to research.
• • Defining and implementing a comprehensive logging, alerting, and monitoring strategy is crucial for tracking model performance, detecting data or concept drift, and ensuring overall system reliability. This proactive monitoring will enable us to maintain the integrity and effectiveness of our deployed models.
• • This role demands a strategic thinker with a hands-on approach, capable of both setting high-level technical direction and diving deep into implementation details when necessary. You will be a key player in scaling our ML capabilities to meet the demands of our rapidly growing enterprise client base.