Senior Machine Learning Engineer
Senior Machine Learning Engineer who designs, deploys, and monitors production-grade ML systems end to end — from data pipelines and feature engineering to containerized training/serving on Kubernetes and Databricks, with experimentation and MLOps. Requires 5+ years with Python, PySpark, MLflow, and cloud platforms; remote from Colombia.
- Compensation
- Not disclosed
- Location
- Colombia
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience
- 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 ML frameworks such as Scikit-learn, PyTorch, TensorFlow, and XGBoost
- Proven experience designing and managing production ML pipelines using MLflow or similar tools
- Experience deploying and operating ML solutions in cloud environments such as AWS, Azure, GCP, or Databricks
- Strong understanding of end-to-end ML lifecycles including data ingestion, training, evaluation, deployment, and monitoring
- Hands-on experience with Docker, Kubernetes, and containerized ML workloads
- Excellent communication skills and ability to influence cross-functional teams
Nice to have
- Experience working with healthcare datasets (claims, eligibility, pharmacy, or EHR data)
- Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, Machine Learning, or related field
- Strong understanding of MLOps practices including CI/CD for ML, model versioning, and automated retraining
- Experience with deep learning for time series forecasting, sequential data, or hierarchical modeling
- Experience designing model evaluation frameworks, experimentation protocols, and performance metrics
- Familiarity with Kubernetes-native ML platforms such as Kubeflow, KServe, or Airflow on Kubernetes
- Experience working in fast-paced, high-growth environments with multiple concurrent priorities
Full description
Job Title: Senior Machine Learning Engineer
Key Skills: Machine Learning, Python, PySpark, Databricks, MLOps, Kubernetes
Experience: 5+ YOE.
Location: Colombia
Mode: Remote.
We at Coforge are hiring Senior Machine Learning Engineer (#15311-1-5) with the following skill set.
Key Responsibilities
· Lead the design and implementation of scalable, production-grade Machine Learning systems in cloud environments.
· Architect and deliver end-to-end ML solutions, from data ingestion and feature engineering to deployment and monitoring.
· Design and manage containerized ML workloads using Docker and Kubernetes for model training, batch inference, and real-time serving.
· Oversee large-scale data pipelines processing multi-terabyte datasets and ensure reliability and performance.
· Lead experimentation strategies, including A/B testing, model validation, and lifecycle management using platforms such as MLflow and Databricks.
· Drive continuous model improvement through automated retraining, model monitoring, bias mitigation, and performance optimization.
· Evaluate and prototype emerging AI/ML technologies, frameworks, and architectures.
· Collaborate with Product, Engineering, Data, and Leadership teams to define and execute ML initiatives aligned with business goals.
· Establish engineering standards, code quality practices, and technical documentation.
· Mentor engineers and provide technical leadership across Machine Learning initiatives.
Required Skills & Qualifications
· Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
· 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 machine learning frameworks such as Scikit-learn, PyTorch, TensorFlow, and XGBoost.
· Proven experience designing and managing production ML pipelines using MLflow or similar tools.
· Experience deploying and operating ML solutions in cloud environments such as AWS, Azure, GCP, or Databricks.
· Strong understanding of end-to-end ML lifecycles, including data 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.