Machine Learning Engineer

Job Summary:

Machine Learning Applications Engineer

Key Responsibilities:

• Design, develop, and deploy scalable ML models and applications into production environments.
• Build and manage end-to-end ML pipelines including data ingestion, model training, evaluation, versioning, deployment, and monitoring.
• Implement CI/CD pipelines tailored for ML workflows.
• Collaborate with data scientists, software engineers, and cloud architects to operationalize machine learning solutions.
• Ensure high availability, reliability, and performance of ML services in production.
• Monitor and optimize model performance post-deployment.
• Automate infrastructure provisioning using Infrastructure-as-Code (IaC) tools.
• Maintain strong documentation of ML systems, experiments, and deployment configurations.

Required Skills & Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• Minimum 6 years of professional experience in software engineering or ML engineering roles.
• Strong hands-on experience with

  machine learning frameworks

  like TensorFlow, PyTorch, or Scikit-learn.
• Proficiency in

  Python

  (and optionally Java, Scala, or Go).
• Solid experience with

  DevOps tools

  such as Docker, Kubernetes, Jenkins, GitLab CI/CD.
• Experience with cloud platforms like

  AWS, Azure, or GCP

  , particularly with AI/ML services and infrastructure.
• Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK, CloudWatch).
• Strong understanding of

  ML Ops practices

  including model versioning, experiment tracking, and reproducibility.

Preferred Qualifications:

• Experience with

  Kubeflow

  ,

  MLflow

  ,

  SageMaker

  , or

  Vertex AI

  .
• Familiarity with

  data engineering tools

  such as Apache Airflow, Spark, or Kafka.
• Understanding of data security and compliance best practices in ML deployments.
• Prior experience in deploying large-scale, low-latency ML applications in production.