GEN AI Date Engineer

Title: Senior AI & Data Engineer (GenAI & LLM Evaluation)

Location: Remote

Engagement: Contact

Role Summary

AI/ML Data Engineer (GenAI & LLM Evaluation)

This is a remote AI and Data Engineering role focused on developing, evaluating, and deploying Generative AI and Large Language Model (LLM) solutions. The position involves building scalable data pipelines, creating model evaluation frameworks and golden datasets, benchmarking LLM performance, and delivering production-ready AI applications. The role requires strong expertise in Python, SQL, machine learning, cloud-based AI services, and data quality frameworks. The successful candidate will collaborate with cross-functional teams to enhance automation, improve operational efficiency, and solve complex business challenges through innovative AI-driven solutions.

Core skills

• 5+ years' experience in Data Engineering, Machine Learning, AI Engineering, or Data Science.
• Strong Python and SQL skills.
• Hands-on experience with Generative AI and Large Language Models (LLMs).
• Experience evaluating LLMs using golden datasets, model benchmarking, or LLM-as-a-Judge techniques.
• Experience building and deploying production-ready ML solutions.
• Strong understanding of data pipelines, validation, and quality frameworks.
• Experience in integrating APIs and cloud-based AI services.

Soft skills

• Strong problem-solving and analytical skills.
• Comfortable owning solutions end-to-end.
• Excellent communication and stakeholder management.
• Experience working in Agile, cross-functional teams.
• Consulting or customer-facing experience is a plus.

What you do

• Build and maintain scalable data pipelines and workflows.
• Develop evaluation frameworks for GenAI and LLM applications.
• Create and maintain golden datasets for model testing.
• Evaluate and benchmark LLM performance.
• Build ML-assisted tools to improve delivery and automation.
• Deploy production-ready AI solutions.
• Work closely with delivery teams to solve technical challenges.
• Continuously improving data quality, automation, and operational efficiency.