Machine Learning Engineer

Position Overview: We are seeking a highly skilled Machine Learning Engineer with expertise in deep reinforcement learning to join our team. The ideal candidate will have a strong background in developing and deploying machine learning models, with specific experience in infrastructure maintenance. This role will involve creating scalable, efficient machine learning pipelines and applying innovative solutions to real-world infrastructure challenges.

Key Responsibilities:

• Machine Learning Model Development: Develop advanced machine learning algorithms and custom data models for cleaning, integrating, and evaluating large datasets, particularly in infrastructure maintenance.
• Deep Reinforcement Learning: Design and implement deep reinforcement learning models that optimize infrastructure maintenance decisions based on various datasets and scenarios.
• End-to-End ML Pipeline Optimization: Build scalable machine learning pipelines for data gathering, processing, feature engineering, model training, and evaluation to support infrastructure projects.
• Model Deployment and Monitoring: Deploy trained models into production environments, ensuring robust monitoring, tracking, and performance optimization for ongoing model improvement.
• Practical Problem-Solving: Tackle real-world infrastructure issues with a focus on delivering practical, data-driven solutions.
• Cross-Functional Collaboration: Collaborate closely with product managers, software engineers, and other team members to integrate machine learning models into broader infrastructure maintenance systems.
• Effective Prioritization: Manage multiple, competing priorities efficiently, ensuring timely delivery of high-impact solutions.
• Strong Communication Skills: Demonstrate excellent written and verbal communication skills, particularly in explaining complex ML concepts to non-technical stakeholders.
• Familiarity with Bayesian Networks and NLP: Leverage knowledge of Bayesian networks and natural language processing (NLP) to enhance the decision-making process and model interpretability.
• Documentation: Create and maintain comprehensive documentation of models, code, and processes to foster knowledge sharing and adherence to best practices.

Qualifications:

•  Minimum of 5 years proven experience in machine learning, particularly deep reinforcement learning, with a focus on infrastructure maintenance or related fields.  
• Strong background in developing and deploying ML models in production environments.
• Expertise in building scalable ML pipelines and workflows.
• Excellent problem-solving skills and the ability to work with large datasets.
• Strong communication skills, with the ability to collaborate across different teams.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, etc

Preferred Qualifications:

• Knowledge and Experience in Image Processing and NLP: Experience working with image data and applying natural language processing techniques to large datasets.
• Big Data Technologies: Familiarity with big data platforms and tools such as Hadoop, Spark, or Hive for large-scale data processing.
• Cloud Platforms: Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud for deploying and managing ML models.