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Senior Machine Learning Engineer

2 weeks ago

Chennai, Tamil Nadu, India beBeeSoftware Full time ₹ 1,80,00,000 - ₹ 2,19,00,000

Job Title: Senior Software Engineering Consultant

Key Responsibilities:
  • Design and implement end-to-end GCP pipelines for ML models from data ingestion to deployment and monitoring.
  • Develop, test, and maintain high-performance Python applications for scalable machine learning systems.
  • Build and optimize CI/CD pipelines tailored for ML workflows.
  • Collaborate with data scientists, DevOps, and product teams to operationalize ML models.
  • Ensure best practices in cloud security, scalability, and performance monitoring within GCP environments.
  • Troubleshoot and maintain deployed ML models in production.
  • Serve as a Tech Anchor, mentoring and guiding junior engineers on the skills required for MLOps.

Requirements:
  • 7+ years of hands-on experience in Python programming.
  • 5+ years of experience in end-to-end GCP deployment and Machine Learning Operations (MLOps).
  • Proven full-stack development experience in frontend development, backend development, and integration with ML Ops pipelines.
  • Strong knowledge of CI/CD pipelines, model deployment, monitoring & scaling ML models in GCP.

Skills:
  • Python (advanced coding and debugging)
  • GCP services (Compute Engine, GKE, BigQuery, Cloud Storage)
  • Tools (Kubeflow, Vertex AI, TensorFlow Extended (TFX), MLflow)
  • Data Pipelines (Apache Airflow / Google Stackdriver)
  • Monitoring tools (Prometheus, Grafana)

As a software engineer consultant, you will have the opportunity to design and develop cutting-edge machine learning solutions using Python and GCP. You will work closely with cross-functional teams to deploy and monitor ML models, ensuring the highest level of performance and security.

To be successful in this role, you should have a strong background in Python programming, with at least 7 years of experience. Additionally, you should have expertise in end-to-end GCP deployment and Machine Learning Operations (MLOps), with at least 5 years of experience. Proven full-stack development experience in frontend development, backend development, and integration with ML Ops pipelines is also essential.

The ideal candidate will have strong knowledge of CI/CD pipelines, model deployment, monitoring & scaling ML models in GCP, as well as proficiency in tools such as Kubeflow, Vertex AI, TensorFlow Extended (TFX), MLflow, Data Pipelines (Apache Airflow / Google Stackdriver) and Monitoring tools (Prometheus, Grafana).