Job Description & How to Apply Below
● 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, Rabbit
MQ, 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, Rabbit
MQ, 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 Tensor Flow, 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.
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