Machine Learning Engineer

Responsibilities

Design and develop machine learning models and algorithms for real-world applications.

Conduct experiments, evaluate results, and continuously optimize model performance.

Collaborate with data scientists and backend engineers to preprocess, clean, and structure data.

Build, maintain, and enhance robust ML pipelines and production-grade systems.

Deploy models into production environments and ensure scalability, reliability, and low latency.

Monitor production systems, identify bottlenecks, and proactively resolve performance issues.

Work with task management and messaging systems like Redis, RabbitMQ, and Celery.

Implement basic CI/CD practices for ML models and pipelines.

Use containerization tools like Docker to streamline deployment and testing.

Qualifications:

Proficiency in programming languages, especially Python.

Strong experience with relational databases such as MSSQL.

Hands-on experience with Redis, RabbitMQ, Celery, or similar tools.

Working knowledge of Docker and containerized environments.

Good understanding of CI/CD principles and practices for ML workflows.

Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or equivalent.

Solid foundational knowledge of statistics, algorithms, and data science concepts.

Ability to troubleshoot complex system issues and optimize system performance.

Strong problem-solving skills, attention to detail, and ability to work collaboratively.