Senior Data Engineer - ML Infrastructure

March 8

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SD Solutions

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SD Solutions is a company that specializes in providing a variety of staffing and outsourcing solutions aimed at driving IT-driven businesses towards growth. The company offers services such as Staff Augmentation, Recruitment Process Outsourcing (RPO), Business Process Outsourcing (BPO), and Employer of Record (EoR), which allow businesses to hire globally without the need to establish a legal entity abroad. SD Solutions focuses on empowering businesses with comprehensive services tailored for global expansion and offers strategic guidance and consulting. They provide dedicated engineering, data science, and DevOps teams to ensure seamless technological evolution. With significant experience and a strong track record, SD Solutions helps businesses scale and transform dynamically.

Technical Support • Business Process Outsourcing • Techninal Support Services • Quality Testing • Dedicated Team Outsourcing

201 - 500 employees

🤝 B2B

🎯 Recruitment

🏢 Enterprise

📋 Description

• Design, build, and maintain robust ETL pipelines that feed our ML models with high-quality data • Develop and maintain APIs for integrating with external LLM providers and other systems • Collaborate closely with MLEs to translate experimental ML code into reliable production pipelines • Implement and optimize data storage solutions with PostgreSQL • Explore and implement caching strategies where beneficial • Contribute to our growing LLM infrastructure and capabilities

🎯 Requirements

• Approximately 5 years of professional experience in data engineering, with a specific focus on ML systems • Strong PostgreSQL experience, including performance optimization, schema design, and query tuning • Proven experience building and maintaining scalable ETL pipelines • Experience developing and maintaining APIs for data services and external integrations • Familiarity with caching mechanisms and when to apply them • Exceptional attention to detail when implementing specifications • Ability to seek clarification rather than making assumptions when translating ML code to production • Interest in large language models and experience integrating with LLM providers • Comfort with cloud environments