AIML Engineer
AIML Engineer
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1 Vacancy
Job Description
- Model Development: Design develop and implement machine learning and deep learning models and algorithms using Python and its associated libraries (e.g. TensorFlow PyTorch scikit-learn).
- Application Integration: Integrate AI/ML models into our production applications and web services writing high-quality maintainable and scalable code primarily in Ruby.
- Data Pipeline Construction: Work with data engineers to build and maintain data processing pipelines for cleaning manipulating and preparing data for model training using Python libraries such as Pandas and NumPy.
- Deployment and MLOps: Deploy and manage AI/ML models in production environments potentially leveraging cloud platforms like AWS Azure or Google Cloud.
- Performance Optimization: Monitor and optimize model performance addressing any issues related to accuracy or efficiency post-deployment.
- Code Quality: Conduct thorough code reviews and adhere to software engineering best practices ensuring the reliability and quality of both Python and Ruby code.
- Collaboration: Work closely with data scientists product managers and other engineering teams to translate business needs into technical requirements and deliver integrated solutions.
- Research and Innovation: Stay current with the latest developments in AI/ML evaluating new technologies and methodologies to enhance our products.
- Education: Bachelors or Masters degree in Computer Science Engineering Data Science or a related field.
- Programming Expertise:
- Python: Proven experience developing and deploying machine learning models and data pipelines. Deep familiarity with key libraries (TensorFlow PyTorch scikit-learn Pandas NumPy).
- Ruby: Strong experience developing scalable web applications and APIs preferably with the Ruby on Rails framework.
- ML Fundamentals: A solid understanding of machine learning algorithms statistical methods and data analysis techniques.
- Mathematics: Strong grasp of mathematical concepts relevant to ML including linear algebra calculus and probability.
- Software Engineering: Familiarity with software architecture design patterns and best practices including version control with Git.
- Problem-Solving: Excellent analytical and problem-solving skills with a creative approach to tackling complex issues.
- Experience with big data tools such as Apache Spark.
- Experience with MLOps tools for model management and deployment.
- Experience with cloud services (AWS Google Cloud or Azure) for deploying AI/ML solutions.
- Familiarity with specific AI fields like Natural Language Processing (NLP) or Computer Vision.
Employment Type
Full-time
