AI Model Optimization Expert

1 week ago

Bengaluru, Karnataka, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 25,00,000

Role Overview

We are seeking a skilled Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning, data manipulation, and cloud platforms.

Key Responsibilities

  • Manage and optimize end-to-end MLOps pipelines for data collection model training, validation, and monitoring while ensuring team collaboration and effective resource allocation.
  • Drive the implementation of model compression, quantization, and distributed training techniques to enhance performance, encouraging innovative solutions from team members.
  • Track key metrics and optimize deployed models to ensure ongoing effectiveness, collaborating with team members to identify improvement opportunities.
  • Collaborate with cloud architects and DevOps teams to design and maintain scalable ML infrastructure, ensuring effective resource management and deployment.
  • Work closely with applied scientists and analysts to transform model requirements into production-ready solutions, facilitating teamwork across departments.
  • Establish and maintain monitoring and alerting systems for deployed models, ensuring prompt issue resolution while guiding the team in best practices.
  • Create and uphold documentation for ML architecture and best practices to ensure knowledge sharing within the team, promoting continuous improvement.
  • Stay current with advancements in ML technologies and lead ongoing enhancement initiatives within the team, encouraging team participation in the ML community.

Requirements

  • Bachelor's/Master's / PhD in Computer Science or related field.
  • 5 years of experience in machine learning with a strong portfolio of deployed ML models for various use cases, including batch streaming and real-time.
  • Proficient in Python for model development and data manipulation with experience in Java or Scala for building production systems.
  • Familiarity with messaging queues (e.g. Kafka SQS) and MLOps tools (e.g. MLflow, Kubeflow, Airflow).
  • Experience with cloud platforms (AWS, Google Cloud, Azure) and containerization technologies (Docker, Kubernetes).
  • Knowledge of machine learning frameworks (e.g. TensorFlow, PyTorch) and databases (e.g. Elasticsearch, MongoDB, PostgreSQL).
  • Understanding of data processing and ETL tools (e.g. Apache Spark, Kafka).
  • Experience with monitoring tools like Grafana and Prometheus.
  • Strong problem-solving skills and an analytical mindset.

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