ML Engineer MLOps

About Aligned Automation

At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future. Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace.

ML Engineer MLOps

Experience: 7 to 9 Years Employment Type: Full-time

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.

Technical Skills:

• 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)

Key skills/competency:

• MLOps
• Machine Learning Pipelines
• Python
• Data Engineering
• ETL
• CI/CD
• Docker
• Kubernetes
• AWS
• Model Deployment