Contract: Senior/Lead ML Operations Engineer

Upwork ($UPWK) is the world’s work marketplace. We serve everyone from one-person startups to over 30% of the Fortune 100 with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential.

Last year, more than $3.3 billion of work was done through Upwork by skilled professionals who are gaining more control by finding work they are passionate about and innovating their careers.

This is an engagement through Upwork’s Hybrid Workforce Solutions (HWS) Team. Our Hybrid Workforce Solutions Team is a global group of professionals that support Upwork’s business. Our HWS team members are located all over the world.

This role is a long-term contract position.

Join our Algorithms and Research team as a Senior/Lead Machine Learning Operations (MLOps) Engineer. We are seeking an experienced engineer passionate about productizing advanced technologies, including Generative AI and Large Language Models (LLMs), to revolutionize Upwork's platform capabilities.

In this role, you will focus on deploying machine learning models, particularly those leveraging graph data structures, and building the infrastructure to support them. You will work closely with machine learning engineers on serving models built using graphs and contribute significantly to our multi-year knowledge graph initiative. This is an opportunity to make a substantial impact on Upwork's search, recommendation, and matching functionalities.

Key Responsibilities

• Lead the development, integration, and maintenance of knowledge graphs, including data modeling, ETL pipelines, and graph database management.
• Deploy, monitor, and optimize machine learning models — especially those leveraging Generative AI and LLMs — at scale in production environments.
• Design and implement retrieval-augmented generation systems to enhance search and recommendation functionalities.
• Build and maintain robust machine learning pipelines, ensuring seamless data flow from ingestion to model serving.
• Develop and integrate APIs for serving ML models and knowledge graph data to platform applications.
• Implement monitoring, alerting, and active learning systems to ensure model performance and reliability.
• Collaborate closely with a globally distributed team of ML engineers, data engineers, and product stakeholders to deliver impactful solutions.
• Continuously drive improvements in model accuracy, system scalability, and operational efficiency.

Must Haves (Required Skills):

• 5+ years of experience deploying and maintaining machine learning models in production environments.
• Proven expertise in knowledge graph development, graph databases (e.g., Neo4j, TigerGraph), and data integration.
• Strong programming skills in Python (preferred), with proficiency in Java or Golang a plus.
• Deep understanding of scalable, cloud-based architectures (AWS preferred; GCP and Azure experience valued).
• Hands-on experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Solid grasp of Generative AI, Large Language Models, and modern ML frameworks.
• Experience building and maintaining ETL pipelines and integrating with relational databases.
• Familiarity with RESTful API development and best practices.
• Excellent problem-solving skills, proactive mindset, and ability to thrive in a remote, collaborative environment.

As a plus:

• Experience with data engineering, API integration, and monitoring/optimization of ML services.
• Prior work on multi-year, large-scale knowledge graph or graph ML projects.
• Exposure to active learning systems and ML model monitoring frameworks.

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