Job Description

In this role, y ou will plan, design, develop and maintain high-performing data pipelines, machine learning services, and cloud infrastructure that power ML-based products You will work closely with Data Engineers and Data Scientists to promote the best experience for data analytics, data science explorations, development of data-based features, and ensure that ML projects run smoothly from experimentation to production.

Your primary focus will be on building robust ML infrastructure, deploying and monitoring traditional ML models, and ensuring seamless integration with our data ecosystem.

Responsibilities:

• Plan, design, develop, and maintain scalable and high-performing data pipelines and ML infrastructure
• Lead the development and deployment of machine learning services and APIs
• Develop and maintain end-to-end (E2E) ML pipelines from data ingestion to model deployment
• Implement and maintain code quality standards, data quality checks, and comprehensive testing frameworks
• Work closely with Data Scientists to translate ML models into production-ready solutions
• Lead the implementation of MLOps best practices, including CI/CD for ML models
• Ensure smooth operation of ML projects and troubleshoot production issues
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
• Drive technical decisions and evaluate new technologies and software products
• Work with GenAI platform framework for all generative AI implementations

Requirements:

• Degree in Computer Engineering, IT, or similar field
• Minimum of 5 years of working experience as a Backend Engineer and/or Machine Learning Engineer
• Fluency in English: Excellent written and verbal communication skills in English
• Strong ability to write robust, production-grade code in Python or similar languages
• Proven experience with code quality, data quality, and testing frameworks
• Experience developing end-to-end ML pipelines and workflows
• Experience developing and deploying APIs for machine learning models
• Proficiency with SQL/NoSQL databases
  Experience with MLOps frameworks such as MLFlow, KubeFlow, or similar
• Experience with AWS solutions, particularly SageMaker and other ML services
• Strong understanding of ML model lifecycle management and deployment strategies
• Knowledge of containerization and orchestration technologies (Docker, Kubernetes)