Key Responsibilities:
Model Development: Design, build, and maintain scaleable machine learning models and algorithms.
Data Analysis: Analyze and pre-process data from various sources to prepare it for model training.
Model Training & Evaluation: Train, validate, and tune machine learning models to achieve optimal performance.
Deployment: Deploy models into production and integrate them with existing systems.
Monitoring & Maintenance: Monitor model performance in production and update models as necessary to ensure they remain accurate and relevant.
Collaboration: Work with cross-functional teams, including data scientists, software engineers, and product managers, to understand requirements and deliver solutions.
Research: Stay current with the latest developments in machine learning and AI to continuously improve our technology stack.
Required Qualifications:
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
Experience: Demonstrated at least 3 years experience as a Machine Learning Engineer or in a similar role.
Programming Skills: Expertise in programming languages such as Python, golang, or Java.
Machine Learning Frameworks: Proficient with machine learning frameworks and libraries like TensorFlow, PyTorch, JAX, scikit-learn, or equivalent.
Data Management: Strong understanding of data management and processing tools such as SQL, Hadoop, Spark, etc.
Problem-Solving: Exceptional analytical and problem-solving abilities.
Data Analysis Tools: Skilled in using data analysis and visualization tools like NumPy, Pandas, and Matplotlib.
NLP Experience: Familiarity with natural language processing concepts and large language models, including transformers and attention mechanisms.
Preferred Qualifications:
Advanced Education: Ph.D. in a relevant field.
Domain Knowledge: Experience in specific domains such as natural language processing (NLP), recommendation systems, computer vision, etc.
Big Data: Experience with big data technologies and distributed computing.
Cloud Services: Familiarity with cloud platforms such as AWS, Google Cloud, or Azure.
DevOps: Knowledge of CI/CD pipelines and tools for automating the deployment of machine learning models.