Innovative Data Scientist

3 days ago

Kolkata, West Bengal, India beBeeMachineLearningEngineer Full time ₹ 1,04,000 - ₹ 1,30,878
Machine Learning Engineer

We are seeking a skilled and experienced Machine Learning Engineer to design, build, and deploy scalable machine learning systems in production.

This is an engineering-focused role where you will partner with data scientists to productionize models, own ML pipelines end-to-end, and drive the reliability, automation, and performance of our critical ML infrastructure. You will work on mission-critical systems where robustness, monitoring, and maintainability are paramount, leveraging modern MLOps tools, cloud platforms, containerization, and model serving at scale.

Key Responsibilities:
  • Design and build robust ML pipelines and services for efficient model training, validation, and seamless deployment into production.
  • Collaborate closely with data scientists, solution architects, DevOps engineers, and other stakeholders to align components and pipelines with overarching project goals and requirements. Communicate any deviations from target architecture effectively.
  • Ensure seamless cloud integration with AWS and Azure cloud services for enhanced performance, scalability, and resource utilization.
  • Build reusable infrastructure components applying best practices from both DevOps and MLOps.
  • Adhere to stringent security standards and regulatory compliance, particularly when handling confidential and sensitive data within ML systems.
  • Design optimal network plans for given Cloud Infrastructure, ensuring compliance with established network security guidelines.
  • Monitor model performance in production and implement automated drift detection and retraining pipelines to maintain model accuracy and relevance.
  • Optimize models for performance, scalability, and cost efficiency through techniques such as batching, quantization, and hardware acceleration.
  • Create detailed documentation and guidelines for the effective use and modification of developed components, fostering knowledge sharing within the team.
Required Skills and Qualifications:
  • Strong programming skills in Python.
  • Deep experience with major ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
  • Hands-on experience with MLOps tools like MLflow, Airflow, TFX, Kubeflow, or BentoML.
  • Proven experience deploying models using Docker and Kubernetes.
  • Strong knowledge of cloud platforms (AWS/GCP/Azure) and their respective ML services (e.g., SageMaker, Vertex AI).
  • Proficiency with data engineering tools including Spark, Kafka, and both SQL/NoSQL databases.
  • Solid understanding of CI/CD (Continuous Integration/Continuous Delivery) principles, version control (Git), and infrastructure as code (Terraform, Helm).
  • Experience with monitoring and logging tools such as Prometheus, Grafana, and ELK stack.
Good-to-Have Skills:
  • Experience with feature stores (e.g., Feast, Tecton) and experiment tracking platforms.
  • Knowledge of edge/embedded ML, model quantization, and optimization techniques.
  • Familiarity with model governance, security, and compliance considerations in ML systems.
  • Exposure to on-device ML or streaming ML use cases.
  • Experience leading cross-functional initiatives or mentoring junior engineers.

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