Senior Machine Learning Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 1d ago
The date the source published, not the day we noticed it (2026-10-09). Last seen at its source 1h ago.
We have tracked this listing since 10 Oct 2026 (1 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
The listing says yes
The location field doesn't say remote, so our assessment is based on the title or the description. Read the listing before applying.
Who may apply?
Colombia
The description agrees: it names Colombia.
What the ad says
…Location: Colombia Mode: Remote…
Pay not stated
Similar roles pay $5,000–7,000/mo
Middle 50% of 9 listings that do state pay — Engineering · Senior · Colombia · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Greenhouse employer's own board first seen 1d ago · last seen 1h ago
The listing
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.
Preferred Skills
· Experience working with healthcare datasets, including claims, eligibility, pharmacy, or EHR data.
· Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, Machine Learning, or a related field.
· Strong understanding of MLOps practices, including CI/CD for Machine Learning, model versioning, and automated retraining.
· Experience with deep learning techniques 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.
Posted On: 09-10-2026
At Coforge, we hire professionals based solely on their skills and qualifications and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.