Machine Learning Engineer

1. Machine Learning Development & Deployment

• Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross-sell/upsell opportunity identification.
• Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.
• Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2. Cross-Functional ML Use Cases

• Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.
• Develop domain-specific models and continuously improve them using feedback loops and real-world performance data.

3. Model Governance and MLOps

• Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.
• Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture

• Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.
• Perform feature selection, transformation, and engineering aligned with each domain’s business logic.

5. Communication & Stakeholder Collaboration

• Present technical insights and model results to business and executive stakeholders in a clear, actionable format.
• Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Required:

• 4+ years of experience in machine learning, data science, or AI engineering, with a strong software engineering foundation.
• Proficiency in Python, and libraries such as scikit-learn, XGBoost, PyTorch, TensorFlow, or similar.
• Experience deploying models into production using ML pipelines and orchestration frameworks.
• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

Preferred:

• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.
• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).
• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).
• Background in statistics, forecasting, optimization, or recommendation systems.