Machine Learning Engineer at EduBulb Consulting

Job Description

• The Machine Learning Engineer will play a crucial role in designing, developing, and deploying state-of-the-art machine learning models and algorithms.
• S/he will collaborate with cross-functional teams to drive innovation, solve complex business challenges, and deliver scalable ML solutions.
• The ideal candidate must possess deep technical expertise in machine learning, strong programming skills, and a proven track record of delivering successful ML project What does the job involve

Responsibilities
Research and Development:

• Conduct in-depth research on cutting-edge machine learning techniques and stay abreast of the latest advancements in the field.
• Explore and experiment with novel ML algorithms, models, and frameworks to address specific business challenges.
• Design, implement, and optimize machine learning models and algorithms to deliver robust and scalable solutions for various applications.
• Leverage data preprocessing techniques and feature engineering to enhance model performance and accuracy.

Data Management:

• Collaborate with data engineers and data scientists to ensure seamless data integration, data quality, and data pipeline efficiency.
• Work with large datasets, both structured and unstructured, to extract valuable insights and patterns.
• Develop robust testing frameworks and methodologies to evaluate the performance of ML models in real-world scenarios.
• Implement A/B testing and statistical analysis to validate the effectiveness of deployed models.

Deployment and Scaling:

• Deploy machine learning models into production environments, considering scalability and maintainability.
• Monitor model performance, conduct periodic updates, and troubleshoot issues to ensure optimal system functionality. Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to drive ML initiatives and achieve project goals.
• Provide technical leadership and mentorship to junior members of the ML engineering team.

Documentation and Communication:

• Document all development processes, methodologies, and experimental results to facilitate knowledge sharing and future enhancements.
• Communicate complex technical concepts and findings to both technical and non-technical stakeholders effectively.
• Perform other duties as assigned.

Qualifications

• Bachelor’s or Master's in Computer Science, Data Science, Machine Learning, or a related field.
• 5+ years' of industry experience as a Machine Learning Engineer, developing and deploying ML models in real-world applications. Expert knowledge with a scripting language (e.g. Python) and with an object-oriented language (e.g. C++, Java).
• Strong understanding of machine learning algorithms, statistical modeling, and optimization techniques.
• Proficiency in the development, validation, implementation, and production launch of machine-learning algorithms and models.
• A passion for implementing coding best practices across a team. Experience with popular ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Solid knowledge of data processing, feature extraction, and data visualization techniques.
• Ability to create logical data models by combining data from multiple sources including internal, and external data.
• Ability to test ideas and adapt methods quickly end to end from data extraction to implementation and validation
• Familiarity with cloud computing platforms and distributed systems for scalable ML deployments.
• Excellent problem-solving, analytical, and communication skills. Ability to work collaboratively in a fast-paced and dynamic environment.
• Deep appreciation for diversity of thought and a proponent for collaborative solutions.
• Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences.

Method of Application