Machine Learning Performance Engineer

Job Description: Role Overview:
We are seeking a seasoned ML Engineer with strong MLOps expertise to lead and scale our machine learning infrastructure. The ideal candidate will be responsible for designing, deploying, and maintaining robust ML pipelines, ensuring model reproducibility, scalability, and performance in production environments.

Key Responsibilities:

• Participate in Agile/Scrum ceremonies and contribute to sprint planning and retrospectives.
• Scope and break down complex ML problems into actionable tasks.
• Write clean, scalable, and production-ready code for ML pipelines and services.
• Design and implement efficient ETL workflows for structured and unstructured data.
• Develop Python scripts for data preprocessing, feature engineering, and automation tasks.
• Submit and manage pull requests; resolve issues flagged by linters/scanners.
• Conduct and participate in code reviews to ensure high-quality standards.
• Collaborate with data scientists, software engineers, and DevOps teams.
• Automate model training, validation, deployment, and monitoring workflows.
• Implement CI/CD pipelines for ML models and data workflows.
• Monitor model performance and data drift in production environments.
• Ensure compliance with data governance and security standards.

• Languages & Frameworks: Python (advanced), SQL
• Data & Storage: PostgreSQL, S3, data versioning tools
• ETL Tools & Practices: Airflow, Pandas, custom Python-based ETLs
• ML & MLOps Tools: MLFlow (tracking, registry, deployment), LLMs (integration and fine-tuning), Hugging Face, TensorFlow/PyTorch (optional)
• Infrastructure & DevOps: AWS ECS, Docker, Kubernetes (preferred), GitHub Actions
• Version Control & Collaboration: GitHub, GitLab
• Monitoring & Logging: Prometheus, Grafana, or similar tools (optional but preferred)