Machine Learning Engineer

About GSPANN:

Headquartered in California, U.S.A., GSPANN is a leading provider of consulting and IT services to global clients. We specialize in helping clients transform their IT capabilities, optimize business practices, and drive operational efficiency across industries such as retail, high-technology, and manufacturing. With five global delivery centers and over 1,900 employees, we combine the personalized approach of a boutique consultancy with the extensive capabilities of a large IT services firm.

Job Title: MLOps Engineer

Job Type: Contract

Job Location: Mexico (100% Remote)

Description:

The Opportunity:

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:

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

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

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.

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.

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.

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.

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

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

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

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

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

We are excited about you if you have experience with the following technologies:

AI Developer Tools (e.g., Claude Code, GitHub Copilot, etc.)

Git, CI/CD, Jenkins, ArgoCD

Python, PySpark, SQL, SparkSQL, Scala, Java

Apache Spark, Databricks, Delta Lake

Scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow, Keras

MLflow (Experiment Tracking, Model Registry, and Model Serving)

Model Explainability and Interpretability tools like SHAP, LIME, etc.

Scientific Computing and Data Manipulation tools (e.g., Pandas, NumPy, SciPy)

Experience with Hyperparameter Optimization tools like Optuna, Hyperopt, Ray Tune, etc.

Experience with Data Quality and Model Monitoring tools.

Feature-engine, Featuretools for Automated Feature Engineering.

RESTful API Design Patterns, FastAPI, Flask, Swagger.

Experience with Pipeline Monitoring and Alerting tools like Grafana, Grafana Loki Logging, Azure Monitor and Application Insights.

Containerization technologies like Docker, Kubernetes.

Experience with cloud platforms (e.g., AWS, Azure, GCP) and their ML services

Experience with markup languages such as JSON and YAML

Knowledge of A/B testing, statistical experiment design, and causal inference is a plus

Additional Qualifications:

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

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

Good communication skill (verbal and written)

Experienced on Agile methodology and tools (Jira, Confluence)

Work experience in the Retail industry is a plus

Working at GSPANN

GSPANN is a diverse, prosperous, and rewarding place to work. We provide competitive benefits, educational assistance, and career growth opportunities to our employees. Every employee is valued for their talent and contribution. Working with us will give you an opportunity to work globally with some of the best brands in the industry.

The company does and will take affirmative action to employ and advance in the employment of individuals with disabilities and protected veterans and to treat qualified individuals without discrimination based on their physical or mental disability status. GSPANN is an equal opportunity employer for minorities/females/veterans/disabled.