MLOps Engineer

Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.

Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.

Type: Remote, Full-time

Role Summary
We are hiring a Senior MLOps Engineer to design, automate, deploy, monitor, and govern production machine learning systems on Azure. The role requires deep expertise in Azure Databricks, MLflow, Databricks Feature Engineering, Azure Machine Learning, PySpark, CI/CD, containerization, and cloud-native software engineering.
The successful candidate will partner with Data Scientists and Data Engineers teams to operationalize machine learning solutions, establish MLOps standards, and build scalable, reliable, secure, and compliant ML platforms. This role focuses on productionization, deployment automation, model lifecycle management, observability, governance, and platform engineering, rather than model development.

Key Responsibilities

• Design, implement, and maintain end-to-end MLOps workflows for model training, deployment, monitoring, retraining, and retirement.
• Productionize data science assets by converting notebooks and prototypes into modular, testable, deployable Python packages and services.
• Build and manage CI/CD pipelines for machine learning models, feature pipelines, and data products.
• Implement model lifecycle management using MLflow, including experiment tracking, model registry, approval workflows, versioning, and rollback.
• Develop and maintain feature engineering pipelines and reusable feature assets using Databricks Feature Engineering and Delta Lake.
• Deploy and operate batch, streaming, and real-time inference workloads using Databricks Model Serving, Azure Machine Learning, and Kubernetes-based platforms.
• Establish automated testing, validation, and release processes for ML code, data, features, and models.
• Implement model monitoring and observability for service health, latency, model performance, drift detection, and operational reliability.
• Ensure governance, security, lineage, auditability, and access controls through Unity Catalog and Azure security services.
• Optimize ML platforms and workloads for scalability, reliability, performance, and cloud cost efficiency.
• Define and promote MLOps best practices, engineering standards, and platform architecture across teams.

Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.

Originally posted on Himalayas