MLOps Engineer

Location:Mexico (100% Remote)

Project:(6-8 months)

We are seeking a highly experienced Machine Learning Engineer to join our MarTech team and play a pivotal role in driving innovation within our ML ecosystem. You will be responsible for the end-to-end development, optimization, and deployment of production-ready ML models and feature engineering pipelines, with a strong emphasis on operationalizing models that power the customer experience. This role demands a strong understanding of ML engineering best practices and proven experience building scalable ML systems and feature pipelines.

Responsibilities:

o Design, develop, and deploy machine learning solutions and feature engineering pipelines.

o Configure, test, debug, deploy, document, and maintain ML pipelines, models and feature engineering modules while adhering to specific development best practices and quality standards.

o Work closely with data scientists, data engineers, and solution architects to develop technical design specifications for ML programs, focusing on efficient feature engineering and model deployment.

o Analyze large-scale datasets and validate the proposed ML solutions with both the architectural design and the business needs, ensuring model performance meets target metrics.

o Responsible for troubleshooting and issue analysis across the ML stack, including feature pipelines, model training, inference, and model monitoring, as well as coding, testing, and implementing model enhancements.

o Demonstrate a strong understanding of supervised, unsupervised, ensemble, and deep learning algorithms to design and implement effective ML solutions, with experience in feature engineering, model evaluation, and continuous performance optimization to meet business targets.

o Implement and maintain MLOps practices including experiment tracking, model versioning, A/B testing, and automated retraining pipelines.

o Thrive in a fast-paced agile development environment, driving iterative model improvements.

o Implement and maintain data governance and model monitoring frameworks to ensure model reliability, fairness, and compliance with business standards.

o Available to support/unblock planned model deployments and retraining cycles during off hours.

o Contribute to the evolution of our ML architecture, with a focus on MLOps principles and emerging technologies for feature stores.

Additional Qualifications:

• Advanced English communication skills required
• Candidates should have hands-on experience deploying and supporting production ML systems, not only research or notebook-based development.

o Bachelor’s or master’s degree in Machine Learning, information technology, Computer Science, or equivalent experience.

o 4+ years of professional experience in a ML engineering capacity with focus on production ML systems.

o Good communication skill (verbal and written)

o Experienced on Agile methodology and tools (Jira, Confluence)

o Work experience in the Retail industry is a plus

Core Stack / Primary Focus Areas

· Machine Learning Engineering

· MLOps & Model Deployment

· Feature Engineering Pipelines

· Python & PySpark

· Apache Spark & Databricks

• ML Frameworks (Scikit-learn, TensorFlow, PyTorch, XGBoost)

· MLflow & Experiment Tracking

· Cloud Platforms (AWS, Azure, GCP)

· Docker & Kubernetes

· REST APIs & FastAPI/Flask

· Model Monitoring & Observability

• Data Engineering & Pipeline Optimization

Must-Have Skills

• Strong experience in Machine Learning Engineering and production ML systems
• Expertise in Python, PySpark, SQL, SparkSQL
• Hands-on experience with Apache Spark, Databricks, and Delta Lake
• Strong experience with Scikit-learn, XGBoost, PyTorch, TensorFlow, and Keras
• Experience building and deploying ML pipelines and feature engineering workflows
• Strong knowledge of supervised, unsupervised, ensemble, and deep learning algorithms
• Experience with MLOps practices including MLflow, model versioning, experiment tracking, model serving, and automated retraining
• Experience with Docker, Kubernetes, and CI/CD pipelines (Git, Jenkins, ArgoCD)
• Experience with REST APIs, FastAPI, Flask, and Swagger
• Experience with monitoring and observability tools such as Grafana, Azure Monitor, and Application Insights
• Strong troubleshooting, debugging, and production support experience
• Experience with cloud platforms such as AWS, Azure, or GCP
• Strong knowledge of Pandas, NumPy, and SciPy
• Experience working in Agile environments using Jira and Confluence
• Strong communication and collaboration skills

Nice-to-Have Skills

· Experience with Scala and Java

• Feature-engine and Featuretools experience
• Hyperparameter optimization tools such as Optuna, Hyperopt, or Ray Tune
• Model explainability tools such as SHAP and LIME
• Data quality and model monitoring tools
• A/B testing, statistical experiment design, and causal inference

· Grafana Loki Logging

• AI developer tools such as GitHub Copilot or Claude Code

· Retail industry experience

JSON and YAML configurations

Originally posted on Himalayas