AI Machine Learning Engineer
Posted Updated
Description
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.