Senior Machine Learning Engineer

1 day ago

Ahmedabad, Gujarat, India beBeeMachineLearning Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
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Job Summary

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  • As a senior machine learning engineer, you will be responsible for developing and maintaining complex machine learning models.
  • You will work with cross-functional teams to design, develop, and deploy scalable machine learning pipelines.
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Key Responsibilities:

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  • Develop and maintain CI/CD pipelines for machine learning models using DevOps tools such as Docker, Kubernetes, and Jenkins.
  • Automate model deployment, monitoring, and scaling processes to ensure high availability and performance.
  • Implement and manage version control for code, data, and models using Git.
  • Ensure data quality, security, and compliance throughout the ML lifecycle.
  • Collaborate with data scientists, engineers, and other stakeholders to design and implement machine learning solutions.
  • Optimize infrastructure for ML workloads to improve performance and reduce costs.
  • Troubleshoot and resolve issues in production ML systems.
  • Implement logging, monitoring, and alerting for ML pipelines using ELK stack, Prometheus, and Grafana.
  • Manage and optimize cloud resources for ML/AI workloads on AWS, Azure, or GCP.
  • Facilitate knowledge sharing and best practices across teams.
  • Mentor peer developers on MLOps/AIOps and DevOps principles.
  • Automate workflows for multi-model deployment on servers and embedded systems.
  • Develop and manage APIs for serving multiple deep learning models efficiently using Flask, FastAPI, or similar.
  • Optimize and convert models for embedded/PC/Android/Server deployment (e.g., TFLite, ONNX, .NEF) with quantization and pruning.
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Required Technical Competencies:

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  • Programming languages: Python or similar.
  • ML frameworks: TensorFlow, PyTorch, scikit-learn.
  • Cloud platforms: AWS, Azure, or GCP.
  • DevOps tools: Docker, Kubernetes, Jenkins, GitLab CI, Azure DevOps.
  • Infrastructure as Code: Terraform, Ansible, CloudFormation.
  • Big data technologies: Spark, Hadoop, Kafka.
  • Monitoring and logging tools: ELK stack, Prometheus, Grafana.
  • Version control: Git.
  • Database management: SQL and NoSQL databases.
  • Data pipeline tools: Airflow, Kubeflow or similar.
  • CI/CD methodologies and tools for ML workflows.
  • Model serving: Flask, FastAPI, TorchServe, TensorFlow Serving.
  • Understanding of ML algorithms and model performance metrics.
  • Knowledge of data privacy and security best practices.
  • Familiarity with MLOps principles and tools: MLflow, DVC, Weights & Biases.
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Benefits

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  • Competitive salary and benefits package.
  • Opportunities for professional growth and development.
  • Collaborative and dynamic work environment.

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