Staff Engineer – Data/AI Platform Engineering

About the Role

As a Staff Engineer – Data / AI Platform Engineering at ABBYY, you will define and drive the architecture of large-scale data and AI platforms that power intelligent automation solutions. This is a hands-on technical leadership role responsible for designing scalable, secure, and production-grade data and ML systems, while influencing architecture and best practices across multiple teams. You will operate at the intersection of data engineering, platform engineering, and applied AI, enabling reliable and compliant machine learning capabilities at scale.

Key Responsibilities

• Architecture & Platform Strategy
  • Define and own the architecture of enterprise data platforms spanning data lakes, document management systems (DMS), and AI pipelines
  • Lead the design and implementation of scalable, secure, and compliant data systems
  • Drive platform strategy across data ingestion, storage, processing, and access layers
  • Influence cross-team technical decisions and platform direction
• Data Governance & Compliance
  • Establish best practices for data governance, cataloging, lineage, and compliance (e.g., Microsoft Purview)
  • Design and implement data anonymization and privacy-preserving pipelines
  • Ensure adherence to data protection and regulatory frameworks (e.g., GDPR)
  • Promote consistency in data quality, versioning, and lifecycle management
• AI / ML Platform Enablement
  • Define architecture for ML model deployment, inference, and lifecycle management
  • Design and scale batch and real-time inference systems
  • Partner with Data Science teams to productionize models and improve reliability
  • Establish standards for MLOps, experiment tracking, and model observability
• Performance, Scalability & Reliability
  • Ensure platform scalability, availability, and performance under production workloads
  • Optimize infrastructure for cost efficiency and resource utilization
  • Drive improvements in system resilience, monitoring, and operational excellence
• Technical Leadership & Mentorship
  • Lead complex, ambiguous, cross-functional initiatives with high impact
  • Mentor engineers and raise the bar on engineering quality and best practices
  • Champion architectural rigor, documentation, and knowledge sharing across teams

Qualifications

• 8+ years of experience in data engineering, platform engineering, or backend systems
• Deep expertise in Python and large-scale data processing systems
• Strong experience designing and implementing data lake architectures and distributed data pipelines
• Proven experience with ML model deployment, inference systems, and MLOps practices
• Hands-on experience with data governance tools (e.g., Purview or equivalent)
• Strong understanding of data privacy, anonymization, and compliance frameworks
• Experience building and scaling ETL/ELT pipelines in production environments
• Solid experience with cloud platforms (Azure preferred)
• Proven ability to lead complex technical initiatives without formal authority

Preferred

• Experience with feature stores and model serving frameworks
• Exposure to NLP, document processing, or OCR/IDP systems
• Experience designing enterprise-grade AI platforms
• Familiarity with regulatory frameworks (e.g., GDPR, SOC2)