MLOPs Engineer only W2

Key Responsibilities: Machine Learning Strategy & Development Lead development of machine learning solutions supporting:

• Recommendation engines
• Customer segmentation
• Churn prediction
• Customer value prediction
• Offer optimization
• Content personalization
• Design, train, evaluate, and deploy predictive models.
• Create reusable ML frameworks and model development standards.

Customer Intelligence & Personalization: Leverage Customer 360 and Identity Graph capabilities to improve prediction accuracy. Develop intelligent customer insights and personalization capabilities. Build models that improve customer engagement, retention, and loyalty outcomes. Collaborate with business leaders to align machine learning priorities with strategic objectives.

AI & Next-Generation Capabilities: Support development of domain-specific AI agents. Partner with engineering teams on AI-powered business experiences. Contribute to LLM-enabled solutions and intelligent assistant initiatives. Evaluate emerging AI capabilities and opportunities for enterprise adoption.

Production ML & Scaling: Work closely with MLOps Engineers to operationalize models. Develop scalable real-time inference solutions. Build monitoring, model validation, and retraining processes. Optimize model performance and reliability in production.

Technical Leadership: Provide technical leadership across Data Science, Engineering, and MLOps teams. Mentor ML Engineers and Data Scientists. Lead architecture discussions and model review sessions. Establish best practices for enterprise AI delivery.

Required Qualifications: 8+ years of Machine Learning Engineering or Applied AI experience.3+ years in a Lead, Principal, or Senior Technical Leadership role. Proven experience delivering production-grade machine learning systems. Strong expertise with:

• DatabricksMLflowPythonMachine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn)
• Real-time inference architectures
• Experience building:
• Recommendation systems
• Personalization engines
• Predictive analytics solutions
• Customer intelligence platforms
• Deep understanding of feature engineering and ML lifecycle management.
• Experience partnering with Data Engineering teams to design ML-ready datasets.