Machine Learning Engineer

Senior Machine Learning Engineer

This role requires a blend of deep machine learning expertise, strong engineering skills, and business domain understanding to turn complex data challenges into actionable, production-grade solutions.

Key Responsibilities:

1.

Machine Learning Development & Deployment

• Design and implement supervised and unsupervised models (e.g., churn prediction, demand forecasting, risk scoring, cross-sell/upsell models).
• Translate business objectives into ML frameworks and deliver scalable production-ready models.
• Build, maintain, and optimize ML pipelines using tools like

  MLflow

  ,

  Airflow

  , or

  Kubeflow

  .

2.

Cross-Functional Business Impact

• Collaborate with stakeholders across:

• Sales

  (lead scoring, next-best action)

• Customer Service

  (sentiment analysis, case deflection)

• Finance

  (revenue forecasting, fraud detection)

• Supply Chain

  (inventory optimization, ETA prediction)

• Order Fulfillment

  (delivery risk modeling)
• Create domain-specific ML solutions with measurable business value.

3.

Model Governance & MLOps

• Implement robust model monitoring, retraining, versioning, and CI/CD workflows.
• Work with DevOps and Data Engineering to deploy and maintain ML solutions in

  AWS

  ,

  Azure

  , or

  GCP

  environments.

4.

Data Engineering & Feature Architecture

• Collaborate with data engineers to define

  feature stores

  , model-ready datasets, and data validation frameworks on platforms like

  Snowflake

  or

  Databricks

  .
• Engineer features that are aligned with business logic and model requirements.

5.

Stakeholder Collaboration & Communication

• Present findings and model insights clearly to business and executive stakeholders.
• Partner with Product Owners and Program Managers to prioritize, scope, and manage delivery of ML initiatives.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

• 4+ years

  of experience in ML/AI engineering or applied data science.
• Proficiency in

  Python

  and key ML libraries such as

  scikit-learn

  ,

  XGBoost

  ,

  TensorFlow

  , or

  PyTorch

  .
• Proven experience deploying models into production using orchestration tools and cloud-native platforms.
• Strong SQL and familiarity with data architectures and cloud services (e.g.,

  AWS SageMaker

  ,

  GCP Vertex AI

  ,

  Azure ML

  ).