Machine Learning Engineer - Technical Lead

Job Title:

Location

Key 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.

Education

• Master 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.

Communication

• Strong verbal and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Experience with big data technologies (e.g., Spark, Hadoop, Kafka).
• Contributions to open-source projects or a strong portfolio of personal projects.