Posted 03 June, 2026
555991 | IC | ML Engineer | Bengaluru
ClifyX
India
Full Time
Reference: 365_594563_26-01696
Design, build, and deploy scalable machine learning models and pipelines to support ad tech features across web/app, CTV, and DOOH. Collaborate with business analysts, data scientists, and developers to ensure models meet business objectives, compliance requirements, and performance standards.
Collaborate with Client, data scientists, and product teams to translate business objectives into ML solutions.
• Design, train, and optimize machine learning models for ad serving, targeting, pacing, frequency capping, and measurement.
• Develop and maintain ML pipelines for campaign management, creative optimization, ad delivery, billing/reconciliation, and reporting.
• Implement MLOps practices: CI/CD for ML, automated retraining, model monitoring, and versioning.
• Partner with data engineering teams to define data schemas, event taxonomies, and feature stores.
• Conduct A/B testing, cohort analysis, and incrementality testing to validate model performance.
• Ensure compliance with GDPR/CCPA/DPDP requirements in model design (consent, data minimization, opt-out flows).
• Integrate ML models with APIs, SDKs, and platform instrumentation.
• Document ML workflows, data flow diagrams, and runbooks for reproducibility.
• Identify bottlenecks (latency, throughput, model drift) and propose improvements.
• Collaborate in agile ceremonies: backlog grooming, sprint planning, UAT, and release readiness.
• Work closely with QA/Testers to validate ML-driven features.
• Champion continuous improvement and adoption of emerging ML/AI technologies.
4–8 years of experience as a Machine Learning Engineer.
• Strong knowledge of ML algorithms (supervised, unsupervised, reinforcement learning) and optimization techniques.
• Proficiency in Python, TensorFlow/PyTorch, Scikit-learn, and ML pipeline frameworks (Kubeflow, MLflow).
• Experience with data engineering tools (Spark, Kafka, Airflow) and cloud platforms (AWS, GCP, Azure).
• Familiarity with ad tech/martech workflows and programmatic advertising.
• Comfort with SQL for data validation and API integrations.
• Hands-on with Agile/Scrum, Jira, Confluence.
• Excellent communication and documentation skills.
• Well-versed with stakeholder communication and documentation.
• Experience with B2C application prediction models.
Good to have:
Exposure to privacy-preserving ML techniques (federated learning, differential privacy).
Familiarity with advanced experimentation frameworks.
Knowledge of dashboarding tools (Power BI, Tableau, Looker) for ML monitoring.
Collaborate with Client, data scientists, and product teams to translate business objectives into ML solutions.
• Design, train, and optimize machine learning models for ad serving, targeting, pacing, frequency capping, and measurement.
• Develop and maintain ML pipelines for campaign management, creative optimization, ad delivery, billing/reconciliation, and reporting.
• Implement MLOps practices: CI/CD for ML, automated retraining, model monitoring, and versioning.
• Partner with data engineering teams to define data schemas, event taxonomies, and feature stores.
• Conduct A/B testing, cohort analysis, and incrementality testing to validate model performance.
• Ensure compliance with GDPR/CCPA/DPDP requirements in model design (consent, data minimization, opt-out flows).
• Integrate ML models with APIs, SDKs, and platform instrumentation.
• Document ML workflows, data flow diagrams, and runbooks for reproducibility.
• Identify bottlenecks (latency, throughput, model drift) and propose improvements.
• Collaborate in agile ceremonies: backlog grooming, sprint planning, UAT, and release readiness.
• Work closely with QA/Testers to validate ML-driven features.
• Champion continuous improvement and adoption of emerging ML/AI technologies.
4–8 years of experience as a Machine Learning Engineer.
• Strong knowledge of ML algorithms (supervised, unsupervised, reinforcement learning) and optimization techniques.
• Proficiency in Python, TensorFlow/PyTorch, Scikit-learn, and ML pipeline frameworks (Kubeflow, MLflow).
• Experience with data engineering tools (Spark, Kafka, Airflow) and cloud platforms (AWS, GCP, Azure).
• Familiarity with ad tech/martech workflows and programmatic advertising.
• Comfort with SQL for data validation and API integrations.
• Hands-on with Agile/Scrum, Jira, Confluence.
• Excellent communication and documentation skills.
• Well-versed with stakeholder communication and documentation.
• Experience with B2C application prediction models.
Good to have:
Exposure to privacy-preserving ML techniques (federated learning, differential privacy).
Familiarity with advanced experimentation frameworks.
Knowledge of dashboarding tools (Power BI, Tableau, Looker) for ML monitoring.
