Senior MLOps Engineer

Accountabilities:

• • ML Infrastructure: Build, maintain, and scale machine learning infrastructure using Databricks, Unity Catalog, feature stores, and related technologies to support the complete ML lifecycle.
  • Drift Detection: Design and implement robust frameworks for detecting data and model drift, enabling proactive monitoring and reliable production performance.
  • Model Calibration & Versioning: Develop model calibration frameworks and establish strong versioning practices that support transparency, reproducibility, and controlled model releases.
  • ML Orchestration: Design and optimize orchestration pipelines for low-latency ML models, including reinforcement learning approaches such as Contextual Bandits and Q-learning.
  • Automated Training: Build automated pipelines and frameworks for model training, retraining, validation, and deployment, improving experimentation speed and operational efficiency.
  • CI/CD for ML: Implement and maintain CI/CD practices for machine learning workflows, integrating Git-based development, Databricks workflows, and automated deployment processes.
  • Production Monitoring: Develop monitoring and operational analytics capabilities to track model performance, identify degradation, and support effective drift mitigation and retraining.
  • Model Lifecycle Management: Establish reliable processes covering model development, testing, deployment, monitoring, versioning, and retirement.
  • ML Scientist Collaboration: Partner closely with ML Scientists to productionize, deploy, operate, and maintain machine learning models and experimentation workflows.
  • Operational Optimization: Continuously improve ML infrastructure, pipelines, and workflows to increase scalability, reliability, efficiency, and deployment velocity.

  Requirements

  • Professional Experience: 7+ years of experience in MLOps, ML Engineering, or closely related roles, with substantial experience deploying and managing machine learning workflows in production.
  • MLOps Expertise: Proven experience building drift detection systems, model calibration frameworks, monitoring solutions, automated retraining workflows, and other production ML infrastructure.
  • Databricks Ecosystem: Strong hands-on experience with Databricks, Apache Spark, MLflow, Unity Catalog, and feature stores.
  • ML Orchestration: Experience deploying and orchestrating low-latency machine learning models, including reinforcement learning solutions such as Contextual Bandits and Q-learning.
  • Training Automation: Strong experience designing automated ML training, validation, retraining, and deployment pipelines with a focus on efficiency and reliability.
  • CI/CD & Git: Strong understanding of Git workflows, CI/CD practices, and tools such as GitLab or equivalent platforms.
  • Programming & Data: Proficiency in Python and SQL, along with strong experience processing large-scale data using Apache Spark or similar technologies.
  • ML Lifecycle Tools: Familiarity with tools such as MLflow, Kubeflow, and Airflow for experiment tracking, workflow orchestration, and ML lifecycle management.
  • Monitoring & Reliability: Deep understanding of model performance monitoring, data and model drift, retraining strategies, and production reliability.
  • Problem-Solving: Strong analytical and troubleshooting abilities, with a proactive approach to identifying infrastructure and model lifecycle challenges.
  • Collaboration: Excellent communication skills and the ability to work effectively with ML Scientists and cross-functional engineering teams.
  • Scalability Mindset: Ability to design robust, automated, and scalable systems capable of supporting complex machine learning workloads in production.

  Benefits

  • Opportunity to work on advanced ML infrastructure supporting dynamic pricing and personalized consumer experiences.
  • Exposure to modern technologies including Databricks, Spark, MLflow, Unity Catalog, feature stores, and reinforcement learning workflows.
  • High-impact role with ownership across the full machine learning lifecycle, from training and experimentation through deployment and monitoring.
  • Collaborative environment with close interaction with ML Scientists and engineering teams.
  • Opportunity to build scalable automation and infrastructure that directly improves model reliability and deployment efficiency.
  • Competitive compensation aligned with experience and market standards.
  • Professional development opportunities through exposure to advanced machine learning and MLOps technologies.
  • Opportunity to contribute to complex, production-scale AI initiatives and continuously improve ML engineering practices.
  • Supportive environment focused on technical excellence, innovation, collaboration, and measurable business impact.