AI Machine Learning Engineer

Ktek Talent Solutions; Pune District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Ktek Talent Solutions is seeking an experienced MLOps Engineer to design, implement, and manage end-to-end ML operations for AI/ML solutions. You will ensure stability and reliability of models and pipelines while governing their lifecycle and monitoring performance.

You will work with data scientists, ML engineers, data engineers, and DevOps teams to implement CI/CD for ML deployments and maintain comprehensive documentation and dashboards. Immediate joining is preferable.

Qualifications

• Hands-on experience in MLOps for AI/ML solutions.
• Experience with MLflow, Databricks, SQL, and cloud ML platforms.
• Experience with dashboards/tools like Power BI/Tableau is preferred.
• Knowledge of model governance and lifecycle management is required.
• Strong data quality, validation, and troubleshooting skills.

Responsibilities

• Design, implement, and manage MLOps workflows and operational processes for AI/ML solutions.
• Oversee stability, reliability, availability, and operational health of ML models and pipelines.
• Manage complete ML model lifecycle: registration, versioning, deployment tracking, lineage, reproducibility, governance, monitoring.
• Implement and maintain monitoring for data drift, concept drift, model performance degradation, inference quality, and pipeline health.
• Develop incident management, recovery, rollback, and escalation procedures for ML production issues.
• Perform data validation and troubleshooting using SQL and data analysis techniques.
• Support data quality, lineage tracking, and governance throughout 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 documentation for model lifecycle, operational processes, incidents, and governance controls.
• Identify operational risks and proactively improve ML platform reliability.
• Participate in production support, root-cause analysis, and continuous improvement initiatives.

Skills

MLOps

MLflow

Databricks

SQL

Power BI/Tableau

Azure ML

Model lifecycle management

Model registry

Model lineage

Deployment tracking

Model monitoring

Data quality and validation

Production support

Git/CI-CD

Tools

CI/CD tooling

Scripting

Job 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 validationProduction 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.