Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients. Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including: ~3 AWS AI/ML Partner of the Year awards ~Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. Position- MLOps Engineer We are looking for an experienced Machine Learning Operations Engineer to lead a newly formed ML Engineering team. You will play a key role in building and maintaining the infrastructure to acquire data from the data platform, deploy models, maintain, monitor and upgrade core data science services in GCP – Vertex AI (essential) and Azure (desirable) that supports the deployment of machine learning models across the enterprise. You’ll work closely with Data Scientists, Platform Engineers, and Developers to ensure seamless integration and scalable, production grade machine learning solutions. This is a hands-on engineering manager role focused on developing APIs, infrastructure, and deployment pipelines for machine learning models. You’ll be expected to write clean, reusable code, follow best practices in cloud and software engineering, and contribute to the operational excellence of our machine learning systems. In addition to strong engineering skills, you’ll bring a solid understanding of Data Science principles. You should be comfortable reading, questioning, and interpreting machine learning models to ensure they are deployed appropriately and effectively. Your ability to bridge the gap between model development and production deployment will be key to delivering robust, high impact machine learning solutions. You’ll be expected to understand and implement methodologies from the ML OPs life cycle. You’ll also be expected to work in an Agile environment, contributing to iterative development cycles, collaborating across disciplines, and adapting quickly to changing requirements. Line Management of the ML Engineers, leading the recruitment and onboarding of new engineers when relevant and identifying gaps in capacity and capability. Oversee your team’s deployment of ML capabilities and provide support to the Head of Data Engineering, specifically around capacity and delivery of the portfolio. As a Team Lead there is an expectation of coaching and mentoring your team members - and supporting the Head of Data Engineering in terms of overall value stream management - especially with partner resources. Coach, mentor and influence ML Engineers into greater ML maturity Experience building a platform as a service product on top of cloud architecture Develop and maintain infrastructure for deploying ML models in both real-time and batch environments. Build and maintain Python APIs (Flask/FastAPI) to serve ML models. Collaborate with cross discipline engineers to integrate ML services into user-facing applications. Work with platform engineers to align with infrastructure best practices and ensure scalable deployments. Review pull requests and contribute to code quality across the MLE team. Monitor and maintain cloud-based ML services, ensuring reliability and performance. Design and implement CI/CD pipelines for ML model deployment. Write unit tests and follow object-oriented programming principles to ensure maintainable code. Support data modelling and cloud networking tasks as needed. Contribute to the development and improvement to our model registry, including tracking and implementation of model discontinuation upgrades and model monitoring. Ownership of the deployment framework for all data science services. You will have oversight of how data will flow into the data science life cycle from the wider business data warehouse Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production Interest and ability to work closely with a team and collaborate on all aspects of the data science and deployment lifecycle Work collaboratively with data scientists, data engineers and other technical teams in order to help support maturation of analytics practice within the organization Writing high quality python code using industry best practice for model training and deployment Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent. ~5+ years as an ML engineer ~ Good understanding of core data science principles and understanding of challenges of migrating research code into production code ~ Hands on experience of GCP and machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments (Neural networks, Random forests etc.) ~ Experience in financial services or insurance with high amounts of regulation is an advantage but not required. ~ Solid experience as a Python developer, ideally in a machine learning engineering context (Flask/FastAPI, OOP, unit testing) ~ Strong understanding of software engineering best practice. ~ Hands on experience with cloud platforms (GCP, AWS, or Azure). ~ Familiarity with containerization using Docker and orchestration of deployments. ~ Understanding of API operations monitoring and logging. ~ Familiarity with Agile methodologies and experience working in Agile teams. ~ Able to articulate on processes and tools utilised to ensure quality, stability, performance, scalability, deployment, security, maintenance and documentation. ~ Creative, proactive, logical and innovative – you do not accept the status quo – and will push hard for innovation and automation. ~ Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale. Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
