Backend Developer

Key Responsibilities:

• Backend Development: Design develop and maintain scalable backend systems services and APIs that handle large datasets and machine learning models.
• Data Pipeline Integration: Collaborate with data scientists to integrate machine learning models and process large volumes of structured and unstructured data efficiently.
• API Development: Develop RESTful APIs to expose data science models and datadriven services to internal and external applications.
• Database Management: Design and optimize database structures for storing and querying large datasets ensuring high performance and scalability.
• Collaboration: Work closely with data engineers data scientists and frontend developers to deliver endtoend data solutions.
• Performance Tuning: Identify bottlenecks and implement solutions for high performance and lowlatency in data processing systems.
• Security: Implement security best practices to protect sensitive data and ensure compliance with relevant regulations.

Required Skills and Qualifications:

• Programming Languages: Proficiency in languages such as Python Java or Node.js.
• Data Science Integration: Familiarity with integrating data science libraries such as TensorFlow Scikitlearn or PyTorch.
• API Development: Experience with building and consuming RESTful APIs and microservices architecture.
• Database Management: Strong knowledge of SQL and NoSQL databases (e.g. PostgreSQL MongoDB).
• Data Pipeline Tools: Experience with data processing tools and frameworks like Apache Kafka Spark or Hadoop.
• Cloud Platforms: Experience with cloud environments like AWS GCP or Azure for deploying datacentric applications.
• Version Control: Proficiency with version control systems like Git.

Preferred Skills:

• Familiarity with containerization (Docker) and orchestration (Kubernetes).
• Experience with data streaming tools such as Apache Flink or Storm.
• Strong understanding of machine learning lifecycle and model deployment.
• Familiarity with data governance privacy laws and data security best practices.

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