Role Overview :

We are looking for a highly skilled MLOps Engineer to design, build, and maintain scalable Machine Learning pipelines that support model development, deployment, monitoring, and automation. The ideal candidate will have solid experience in cloud-based ML workflows, CI/CD practices, orchestration frameworks, and collaboration with cross-functional teams.

Key Accountabilities / Responsibilities :

ML Pipeline Development & Automation :

- Understand business and technical requirements for ML pipelines and implement solutions accordingly.

- Develop, deploy, and maintain end-to-end ML pipelines using Python and related ML frameworks.

- Automate ML pipelines for scheduling, monitoring, logging, alerting, and resiliency.

- Ensure pipelines are optimized for scalability, reliability, and performance.

Collaboration & Delivery :

- Work closely with analysts, data engineers, data scientists, and visualization teams to align ML pipeline development with overall solution delivery.

- Participate actively in Agile ceremonies, including sprint planning, stand-ups, and retrospectives.

Code Quality & Documentation :

- Write robust, clean, maintainable, and well-structured code following best practices.

- Create and update documentation for development processes, operational procedures, and pipeline architecture.

- Participate in code reviews, offering feedback and guidance to team members.

Monitoring & Production Support :

- Monitor ML pipelines in production to ensure high availability and timely execution.

- Quickly investigate, diagnose, and resolve production issues or failures.

- Continuously improve pipeline stability, reliability, and automation.

Required Qualifications & Skills :

Technical Skills :

- Strong experience in developing ML pipelines using Python, including libraries such as pandas, scikit-learn, numpy, etc.

- Hands-on experience building and deploying ML pipelines in cloud environments (GCP preferred). Experience with AWS or Azure is also acceptable.

- Solid understanding of CI/CD tools such as GitHub Workflows, GitLab CI, Jenkins, or similar.

- Strong expertise with orchestration frameworks like Apache Airflow, Prefect, or Dagster.

- Familiarity with containerization and deployment technologies such as Docker, Kubernetes (nice to have).

Soft Skills :

- Excellent verbal and written communication skills to work with both technical and non-technical stakeholders.

- Ability to collaborate effectively in cross-functional Agile teams (Kanban / Scrum).

- Strong problem-solving abilities, attention to detail, and ownership mindset.

Preferred Qualifications (Good to Have) :

- Experience with ML model deployment and monitoring tools (MLflow, Vertex AI, Sagemaker, etc.).

- Knowledge of data engineering workflows and ETL tools.

- Experience working in large-scale, distributed data environments.