Machine Learning Engineer

Title: Machine Learning Engineer

Location: Hybrid (Buenos Aires) - 2-3 Days Onsite

About the Role

We are seeking a proactive Machine Learning Engineer to develop and deploy AI/ML models within our organization. This role focuses on the technical implementation of AI solutions, working closely with data scientists and solution architects. You will be responsible for building, training, and deploying machine learning models, as well as maintaining and optimizing existing systems.

Responsibilities

Model Development & Training:

• Develop and train machine learning models using various algorithms and techniques.
• Optimize model performance and accuracy.
• Implement and maintain data pipelines for model training and evaluation.

Deployment & Scaling:

• Deploy machine learning models at scale, preferably in cloud-native environments.
• Ensure model scalability and reliability in production.
• Monitor model performance and retrain as needed.

Technical Implementation:

• Translate data science prototypes into production-ready ML systems.
• Collaborate with engineering teams to integrate ML models into applications.
• Address technical challenges and implement best practices for ML deployment.

Technology & Trends:

• Stay updated on AI/ML trends, including Generative AI and LLMs.
• Understand and apply various ML approaches and deployment strategies.
• Work with data science and engineering teams to leverage new technologies.

Data & Governance:

• Ensure data quality and integrity for model training and evaluation.
• Contribute to model monitoring and governance processes.
• Support decisions on AI/ML tools and platforms.

Qualifications

• 7+ years in software engineering, data engineering, or related roles (2+ years in AI/ML).
• Experience working with data science and technology teams.
• Experience deploying AI/ML solutions at scale (cloud-native preferred).
• Familiarity with Generative AI, LLMs, and related technologies.
• Strong programming skills (e.g., Python, Java).
• Understanding of data pipelines, data governance, and the ML lifecycle.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch).

Preferred Qualifications

• Experience in cross-functional environments.
• Exposure to MLOps frameworks.
• Experience with containerization and orchestration (e.g., Docker, Kubernetes).
• Master's degree in Computer Science, Data Science, or related field.