AI Machine Learning Engineer

Role & responsibilities

• Design, implement, and manage MLOps workflows, tools, and operational processes for AI/ML solutions.

• Oversee the day-to-day stability, reliability, availability, and operational health of ML models and pipelines.

• Manage the complete ML model lifecycle, including:

• Model registration

• Versioning

• Deployment tracking

• Model lineage

• Reproducibility

• Governance

• Monitoring

• Implement and maintain monitoring for:

• Data drift

• Concept drift

• Model performance degradation

• Inference quality

• Pipeline health

• Service-level issues

• Develop and execute incident management, recovery, rollback, and escalation procedures for ML-related production issues.

• Perform data validation and troubleshooting using SQL and data analysis techniques.

• Support data quality, lineage tracking, and governance practices throughout the ML lifecycle.

• Build and maintain dashboards and operational reports using Power BI, Tableau, Databricks SQL dashboards, or equivalent tools.

• Collaborate with data scientists, ML engineers, data engineers, DevOps/MLOps teams, and business stakeholders.

• Support CI/CD and production deployment processes for ML models and pipelines.

• Maintain appropriate documentation for model lifecycle, operational processes, incidents, and governance controls.

• Identify operational risks and proactively implement measures to improve ML platform and model reliability.

• Participate in production support, troubleshooting, root-cause analysis, and continuous improvement initiatives.

• Work closely with stakeholders to communicate technical issues, risks, dependencies, and proposed solutions.

Mandatory Technical Skills

Candidates must have strong hands-on experience in the following areas:

• MLOps
• MLflow
• Databricks
• SQL
• Power BI / Tableau
• Cloud ML platforms such as Azure ML or equivalent
• Model lifecycle management
• Model registry and versioning
• Model lineage and reproducibility
• Deployment tracking
• Model and pipeline monitoring
• Data quality and validation
• Production support and troubleshooting

Additional Technical Skills

• Strong SQL and data analysis skills for:

• Data validation

• Troubleshooting

• Monitoring

• Reporting

• Root-cause analysis

• Experience with monitoring data drift, concept drift, model performance, and inference quality.

• Working knowledge of Git, CI/CD, scripting, and production support practices.

• Experience with dashboards and alerting using Power BI, Tableau, Databricks SQL, or equivalent platforms.

• Understanding of ML/data development and deployment processes.

• Familiarity with cloud-based ML and data platforms.

Preferred candidate profile

• Have 811 years of overall experience, with strong relevant MLOps/ML engineering experience.
• Have hands-on experience managing production ML models and pipelines.
• Demonstrate strong expertise in MLflow, Databricks, SQL, and cloud ML platforms.
• Understand model governance, lifecycle management, monitoring, and production operations.
• Have excellent communication skills and confidence interacting directly with clients and stakeholders.
• Demonstrate good career stability, preferably with at least 2 years in each organization.
• Be available to join immediately or within 45 days.