Data Engineer | $60/hr Remote

At Crossing Hurdles, we work as a referral partner. We refer candidates to our client that collaborates with the world’s leading AI research labs to build and train cutting-edge AI models.

Position: Data Engineer – AI Trainer

Type: Contract

Compensation: $30–$60/hr

Location: Remote

Duration: 10–40 hrs/week, flexible and asynchronous

Requirements: (Training support will be provided)

• Strong experience in data engineering and large-scale data systems
• Hands-on expertise with big data technologies such as Hadoop and Spark
• Experience building and maintaining scalable data pipelines (ETL/ELT)
• Proficiency with real-time data streaming tools like Kafka
• Experience working with cloud platforms (AWS, GCP, Azure, or similar)
• Familiarity with AI/LLM applications, data curation, and prompt engineering
• Strong problem-solving and troubleshooting skills in distributed systems
• Excellent written and verbal communication skills
• Comfortable collaborating in fully remote, cross-functional teams
• Ability to work independently and manage tasks asynchronously

Preferred:

• Prior experience working as an AI Trainer or on AI/ML-focused projects
• Exposure to generative AI systems and LLM-driven data workflows
• Advanced degree in Computer Science, Data Engineering, or related field
• Cloud or big data certifications (AWS, GCP, Azure, Hadoop, Spark, etc.)
• Experience documenting technical workflows for training or onboarding

Role Responsibilities:

• Design, develop, and optimize large-scale data pipelines using Hadoop and Spark
• Build and maintain robust data architectures to support AI model training
• Integrate and manage real-time data streams using Kafka
• Deploy, orchestrate, and monitor distributed data workloads on cloud platforms
• Collaborate with data scientists and ML engineers to support AI initiatives
• Curate and prepare high-quality datasets for AI and LLM training use cases
• Document data workflows, pipelines, and best practices clearly
• Ensure data security, scalability, performance, and reliability across systems
• Support AI training efforts by validating data quality and pipeline outputs

Application process: (Takes 7–30 mins)

• Upload resume
• AI interview (camera on, skill-based questions; coding for engineers)
• Submit form