Position Profile

We are seeking a Senior Machine Learning Engineer to lead the full lifecycle of AI solutions. The ideal profile combines advanced technical mastery of predictive models with the strategic capability to scale solutions to production in highly complex environments. We are looking for someone capable of transforming raw data into business assets through robust, scalable, and maintainable models.

Main Responsibilities

• ML Architecture: Design, develop, and deploy large-scale machine learning models into production environments.
• End-to-End Lifecycle: Lead the process from data ingestion and feature engineering to training, validation, and monitoring of models.
• MLOps & Scalability: Implement and optimize MLOps pipelines to ensure reproducibility, versioning, and deployment efficiency.
• Technical Collaboration: Work side-by-side with Data Engineering and Software Engineering teams to integrate models into final products.
• Mentoring: Drive the team’s technical growth, establishing quality standards and best practices in AI development.
• Optimization: Research and apply state-of-the-art algorithms to solve critical business problems.

Exclusionary Requirements

• Experience: 5+ years in Machine Learning Engineering or Data Science roles with a focus on production.
• Tech Stack: Advanced proficiency in Python and its scientific libraries (Pandas, NumPy, Scikit-Learn).
• Deep Learning: Solid experience with frameworks such as PyTorch or TensorFlow/Keras.
• Cloud & Infrastructure: Hands-on experience in cloud environments (AWS, GCP, or Azure) using native ML services (e.g., SageMaker, Vertex AI).
• Software Engineering: Strong knowledge of SQL, API design for model serving, and software architecture principles.
• MLOps: Fluent in model versioning and monitoring tools (MLflow, Kubeflow, Weights & Biases, or similar).
• Version Control: Advanced mastery of Git and GitHub/GitLab workflows.

Desirable (Plus)

• Big Data: Experience with distributed processing technologies (Apache Spark, Databricks).
• Containerization: Mastery of Docker and Kubernetes applied to model deployment.
• Optimization: Experience with model quantization or pruning techniques to optimize performance in production.
• Soft Skills: Demonstrated ability to translate ambiguous business problems into clear technical requirements.
• Community: Active participation in the AI community, conferences, or technical publications.