We are looking for a senior member of a team who will engineer the future of our company by building an autonomous and intelligent backbone for our core business processes.
You will be a technical leader and systems builder, tasked with solving our most complex challenges in production-grade ML. You will not only have the autonomy to architect and own the most critical ML services that redefine efficiency, but you will also mentor other engineers and elevate the technical standards for the entire team. Your key stakeholders will be business and product leaders, as well as other engineers who will look to you for guidance.
What we expect from you:Lead:
The architectural design and technical strategy for complex, business-critical ML systems.
The establishment and adoption of MLOps best practices across the team, elevating the quality and reliability of our engineering work.
The mentorship of other engineers through exemplary code reviews, design discussions, and technical guidance.
Own:
The end-to-end lifecycle of the most complex ML services that drive our core business functions, from system architecture to operational excellence.
The design, build, and fine-tuning of foundational predictive engines, including models for forecasting, planning, and process optimization.
Partner with:
Business and product leaders to translate ambiguous, high-impact business problems into well-defined machine learning solutions.
Support:
The automation of decisions and minimization of manual intervention in core business processes through the use of AI and machine learning.
The continuous enhancement of our business capabilities by proactively exploring, prototyping, and advocating for new ML/AI trends and technologies.
Foundational Experience: A strong background in machine learning or software engineering with at least 6 years of relevant experience, and expert proficiency in Python and SQL.
Core ML Expertise: Proven, hands-on experience architecting and deploying complex ML systems in production. This includes deep expertise in applying general ML methods to business challenges and specific experience with time-series forecasting models for real-world applications.
Production & MLOps Skills: Deep experience deploying, monitoring, and maintaining ML systems in a production environment on a major cloud platform (GCP preferred). Expert knowledge of MLOps principles, containerization (Docker, Kubernetes), and CI/CD for machine learning.
Strategic & Collaborative Skills: A pragmatic, product-oriented mindset with a focus on delivering practical solutions that drive business value. Excellent communication skills, with the ability to articulate complex technical concepts and strategy to diverse stakeholders.
Experience designing and building large-scale, distributed ML systems.
Experience combining traditional ML methods with LLMs in a production environment.
A Master's degree or Ph.D. in a relevant field like Computer Science, Statistics, or Mathematical Statistics.
