Responsibilities
- Coding: Write clean, efficient, and well-documented Python code adhering to OOP principles (encapsulation, inheritance, polymorphism, abstraction). Experience with Python and related libraries (e.g., TensorFlow, PyTorch, Scikit-Learn). They are responsible for the entire ML pipeline, from data ingestion and preprocessing to model training, evaluation, and deployment
- End-to-End ML Application Development: Design, development, and deployment of machine learning models and intelligent systems into production environments, ensuring they are robust, scalable, and performant.
- Software Design & Architecture: Apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services. Contribute significantly to the architectural decisions for our ML platform and applications.
- Data Engineering for ML: Design and implement data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference.
- MLOps & Productionization: Establish and implement best practices for MLOps, including CI/CD for ML, automated testing, model versioning, monitoring (performance, drift, bias), and alerting systems for production ML models.
- Performance & Scalability: Identify and resolve performance bottlenecks in ML systems. Ensure the scalability and reliability of deployed models under varying load conditions.
- Documentation: Create clear and comprehensive documentation for ML models, pipelines, and services.
Basic qualifications
- Education: Masters degree in computer science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role.
- Must have Programming Skills: Expert-level proficiency in Python, including experience with writing production-grade, clean, efficient, and well-documented code. Experience with other languages (e.g., Java, Go, C++) is a plus.
- Strong Software Engineering Fundamentals: Deep understanding of software design patterns, data structures, algorithms, object-oriented programming, and distributed systems.
- Good to have Machine Learning Expertise:
- Solid theoretical and practical understanding of various machine learning algorithms
- Proficiency with ML frameworks such as PyTorch, Scikit-learn.
- Experience with feature engineering, model evaluation metrics, and hyperparameter tuning
- Data Handling: Experience with SQL and NoSQL databases, data warehousing concepts, and processing large datasets.
- Problem-Solving: Excellent analytical and problem-solving skills, with a pragmatic approach to delivering solutions.
- Communication: Strong verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
Preferred qualifications
- Experience with big data technologies (e.g., Spark, Hadoop, Kafka).
- Contributions to open-source projects or a strong portfolio of personal projects.
Tags & focus areas
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