7 hours ago
Remote, Colombia or Cali, ColombiaSenior
Responsibilities
- Design and implement scalable, production-grade machine learning systems in cloud environments.
- Deliver end-to-end ML solutions covering data ingestion, feature engineering, deployment, and monitoring.
- Design and manage Docker- and Kubernetes-based ML workloads for training, batch inference, and real-time serving.
- Oversee large-scale data pipelines processing multi-terabyte datasets while ensuring reliability and performance.
- Lead experimentation, A/B testing, model validation, and lifecycle management using MLflow and Databricks.
- Drive model improvement through automated retraining, monitoring, bias mitigation, and performance optimization.
- Evaluate and prototype emerging AI/ML technologies, frameworks, and architectures.
- Collaborate with Product, Engineering, Data, and Leadership teams on ML initiatives.
- Establish engineering standards, code quality practices, and technical documentation.
- Mentor engineers and provide technical leadership across machine learning initiatives.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
- At least 5 years of industry experience building, deploying, and scaling machine learning systems.
- Deep expertise in Python, SQL, and PySpark for distributed data processing.
- Hands-on experience with Scikit-learn, PyTorch, TensorFlow, and XGBoost.
- Experience designing and managing production ML pipelines using MLflow or similar tools.
- Experience deploying and operating ML solutions in AWS, Azure, GCP, or Databricks environments.
- Strong understanding of the complete ML lifecycle, including ingestion, training, evaluation, deployment, and monitoring.
- Hands-on experience with Docker, Kubernetes, and containerized ML workloads.
- Excellent communication skills and the ability to influence cross-functional teams.
- Preferred experience with healthcare datasets such as claims, eligibility, pharmacy, or EHR data.
- An advanced degree in Computer Science, Data Science, Machine Learning, or a related field is preferred.
- Preferred experience with MLOps, model versioning, automated retraining, deep learning, time series forecasting, sequential data, hierarchical modeling, and model evaluation frameworks.
- Familiarity with Kubeflow, KServe, or Airflow on Kubernetes is preferred.
Benefits
- Remote work arrangement from Colombia.
Tech Stack
Apache AirflowAWSAzureDatabricksDockerGoogle Cloud PlatformKubernetesMLflowPythonPyTorchscikit-learnSQLTensorFlowXGBoost
Categories
About Encora
Encora provides software and digital product engineering services for technology companies and enterprises, including cloud-native development, data engineering, and QA. It operates a global delivery model and supports outsourced product development and managed engineering teams. Headquartered in Santa Clara, California, Encora is privately held and was acquired by Coforge; it previously raised $200 million in private equity funding in 2019.