AI Data Engineer - Synthetic Data Generation

Key Responsibilities

Synthetic Data Generation & Quality Assurance

• Design and implement scalable synthetic data generation systems to support model training
• Develop and maintain data quality validation pipelines ensuring synthetic data meets training requirements
• Build automated testing frameworks for synthetic data generation workflows
• Collaborate with ML teams to optimize synthetic data for model performance

APIs & Integration

• Develop and maintain

  REST API integrations

  across multiple enterprise platforms
• Implement robust

  data exchange, transformation, and synchronisation

  logic between systems
• Ensure

  error handling, retries, and monitoring

  for all integration workflows

Data Quality & Testing

• Implement

  automated data validation and testing frameworks

  for ETL and synthetic data workflows
• Translate

  data quality feedback

  from stakeholders into pipeline or generation process improvements
• Proactively monitor and maintain

  data consistency

  across systems

Multi-System Integration & MCP Development

• Build and maintain tool registries for Model Control Protocol (MCP) integration across multiple enterprise systems
• Develop robust APIs for multi-system communication through MCP frameworks
• Design and implement workflows that coordinate multi-system interactions
• Ensure reliable data flow and error handling across distributed system architectures

Cross-Functional Collaboration & Production Integration

• Partner with domain specialists to translate plan execution feedback into actionable insights
• Work closely with Product Managers to align synthetic data generation with business requirements
• Collaborate with Core Engineering teams to ensure seamless production deployment
• Establish feedback mechanisms between synthetic data systems and production environments
  

Required Qualifications

Technical Skills

• Programming:

  Proficiency in Python, Typescript (optional)

• Data Engineering:

  Experience in data engineering frameworks and libraries (Pandas, Apache Airflow, Prefect)

• APIs & Integration:

  Strong background in REST APIs and system integration

• Databases:

  Experience with relational and NoSQL databases (PostgreSQL, MongoDB)

• Cloud Platforms:

  Hands on experience with AWS/GCP/Azure

Experience Requirements

• 2+ years experience in building production-scale data pipelines and orchestration systems
• Demonstrated success in cross-functional collaboration in technical environments

Preferred Qualifications

• Familiarity with managing Kubernetes-based production workloads and workflow orchestration (Argo)
• Familiarity with containerisation and orchestration with tools like Docker, Kubernetes etc.
• Familiarity with synthetic or large-scale data generation
• Background in enterprise software integration
• Experience with Model Control Protocol (MCP) or similar orchestration frameworks
• Knowledge of automated testing frameworks for data pipelines
  

What We Offer

• Lots of learning

  many systems are being built from the ground up, with no existing references or open-source projects to rely on. This will be the first time not just for you, but for the industry as well.
• Opportunity to work at the forefront of enterprise-scale synthetic data generation
• Collaborative environment with product teams, engineering, and domain specialists
• Competitive compensation and comprehensive benefits
• Professional development opportunities in cutting-edge data engineering and Kubernetes orchestration
  

Team Structure

You'll report to the AI Engineering Lead and work closely with:

• ML Engineers developing foundation models
• Product Managers defining business requirements
• Product Specialists providing domain expertise
• Backend Engineers handling production infrastructure
  

This role offers significant impact on our data capabilities and the opportunity to shape how we generate and utilize synthetic data for training enterprise systems.