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**.