About this role
The team at Coforge builds scalable machine learning solutions and robust production-grade ML systems that support large-scale enterprise applications. This Senior Machine Learning Engineer will focus on designing and deploying end-to-end machine learning pipelines, optimizing distributed data workflows, and managing containerized workloads across major cloud platforms.
This role exists to bridge the gap between complex business requirements and high-performance technical execution by collaborating closely with data scientists, engineers, and product teams. A successful candidate will bring strong software engineering fundamentals, deep experience with MLOps best practices, and a proven ability to maintain reliable machine learning infrastructure in production environments.
What you will do
- Design, build, and deploy scalable machine learning solutions into live production environments.
- Construct and optimize end-to-end ML pipelines spanning data ingestion, feature engineering, training, and monitoring.
- Write distributed data processing workflows utilizing PySpark and SQL for large-scale datasets.
- Partner with data scientists, data engineers, and product stakeholders to translate requirements into technical solutions.
- Deploy and manage models across cloud platforms including AWS, Azure, GCP, and Databricks.
- Implement MLOps standards covering versioning, experiment tracking, CI/CD, and lifecycle management.
- Containerize machine learning workloads with Kubernetes to handle model serving and batch processing.
What you need
- Hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or possess equivalent practical experience.
- Demonstrate at least 5 years of professional experience as a Machine Learning Engineer working on production-grade systems.
- Exhibit strong programming proficiency in Python, SQL, and PySpark for large-scale data handling.
- Bring hands-on experience utilizing machine learning frameworks such as Scikit-learn, PyTorch, TensorFlow, and XGBoost.
- Show proven expertise in building and maintaining robust ML pipelines with tools like MLflow.
- Possess practical experience deploying machine learning solutions across cloud environments like AWS, Azure, GCP, and Databricks.
- Maintain a strong grasp of the complete ML lifecycle from initial data preparation to ongoing production monitoring.
Nice to have
- Experience with MLOps, CI/CD pipelines, and model governance frameworks
- Knowledge of model monitoring, observability, and performance optimization techniques
- Experience working in Agile development environments
- Familiarity with Docker, workflow orchestration tools, and distributed computing platforms
- Exposure to generative AI, LLMs, or advanced machine learning systems
Skills and tech stack
Questions candidates ask
What is the work location and modality for this role?
The position is remote and open to candidates located in LATAM, specifically Guatemala, Honduras, El Salvador, Nicaragua, Panama, Colombia, Perú, Mexico, Costa Rica, Brazil, Ecuador, Paraguay, and Uruguay.
What tools and cloud platforms will I use?
You will work with Python, SQL, PySpark, Scikit-learn, PyTorch, XGBoost, TensorFlow, MLflow, Databricks, Kubernetes, and Docker, deploying across AWS, Azure, and GCP.
Who will I be working with on a regular basis?
You will collaborate with Data Scientists, Data Engineers, Product Teams, and business stakeholders to translate business requirements into machine learning solutions.
What are the core technical duties for this position?
Core duties include designing scalable machine learning solutions, building end-to-end ML pipelines, developing distributed data workflows, and implementing MLOps best practices.