Job Opportunities API

Join ABBYY and be part of a team that celebrates your unique work style. With flexible work options, a supportive team, and rewards that reflect your value, you can focus on what matters most – driving your growth, while fueling ours. Our commitment to respect, transparency, and simplicity means you can trust us to always choose to do the right thing. As a trusted partner for purpose-built AI and intelligent automation, we solve highly complex problems for our enterprise customers and put their information to work to transform the way they do business.  Over 10,000 customers trust ABBYY, including many Fortune 500 ones. You will work on further developing a portfolio already containing client names such as DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK. 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 p