ML Ops Engineer

Essential functions

• Design, develop, and maintain scalable machine learning pipelines using Google Cloud Platform (GCP) services or similar AWS/Azure offerings.

• Implement MLOps practices for model versioning, data versioning, experiment tracking, and pipeline orchestration.

• Deploy and manage machine learning models in production environments, ensuring optimal performance and scalability.

• Collaborate with data scientists and engineers to build end-to-end ML solutions.

Qualifications

• Proficiency in Python.

• Competent knowledge of best practices for software development.

• Strong understanding of Data Science concepts such as supervised and unsupervised learning, feature engineering and ETL processes, classical DS models types and neural networks types, hyperparameters tuning, model evaluation and selection.

• Proficiency in usage of appropriate GCP services (or similar AWS or Azure services) for building end-to-end ML pipelines, e.g. Vertex AI, BigQuery, Dataflow, Cloud SQL, Dataproc, Cloud Functions, Google Kubernetes Engine.

• Competent knowledge of MLOps paradigm and practices. Experience with MLOps tools (or appropriate cloud services), including model and data versioning and experiment tracking (e.g., DVC, MLflow, Weights & Biases), pipeline orchestration (e.g., Apache Airflow, Kubeflow). Understanding of deployment strategies for different types of models and inference (batch/online).

• Knowledge and experience with big data processing frameworks (e.g., Apache Spark, Apache Kafka, Apache Hadoop).

• Competent SQL skills and experience with databases like MySQL, Postgres, Redis.

• Experience in developing and integrating RESTful APIs for ML model serving (e.g., Flask and FastAPI).

• Experience with containerization technologies like Docker and orchestration tools (e.g., Kubernetes).

Would be a plus

• Knowledge of monitoring and logging tools (e.g., Grafana, ELK Stack or appropriate cloud services).
• Understanding of CI/CD principles and tools (e.g., Jenkins, GitLab CI) for automating the testing and deployment of machine learning models and applications.
• Experience with Cloud Identity and Access Management.
• Experience with Cloud Load Balancing.
• Knowledge of Infrastructure as Code (IaC) tools such as Terraform and Ansible.

We offer

• Flexible working hours (full-time).

• One "Flex Day" off per month.

• 10 business days of vacation.

• Swiss Medical health coverage.

• Permanent contract with salary updates each 4 months (PESOS ARG).

• Access to Udemy and Platzi for professional training.

• Employee Assistance Program (financial, nutritional, psychological support, etc.).

• Fully covered English classes during working hours.

• Discounts on Club de Beneficios and Samsung products.

• Birthday day off.

About us

Mobile Computing is joining Grid Dynamics (NASDAQ: GDYN), a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.