Staff Engineer – Data/AI Platform Engineering
About this role
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 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)
Here are some of our local benefits:
- Comprehensive medical, accidental, and life insurance
- Weekly wellness sessions to support your physical and mental well-being
- A generous paid time off policy
Join ABBYY, and you will:
Love how you work
- We provide remote and hybrid working options to fit all lifestyles.
- We use flexible hours across most of our teams to allow you to find your own definition of balance.
- Encouraging a culture of giving, we provide two paid volunteering days off every year so you can take time to contribute to the causes you care about.
- To ensure your family is cared for, we offer paid parental leave in all our locations.
Love whom you work with
- We are a global team of 600+ colleagues, spread across 15 countries on four continents.
- With colleagues representing 30+ nationalities, our workforce reflects the world.
- Innovation and excellence run through our veins. Our teams gather the expertise which has garnered ABBYY more than 140 technology patents.
- We are guided by the values of respect, transparency, and simplicity.
- "Team Environment" is in the top three highest-scoring drivers of engagement across all of our departments.
Love what you work on
- We are a company with more than 35 years of experience in the technology market;
- Over 10,000 customers trust ABBYY, including many Fortune 500 ones, with names such as DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK;
- We have modernized the capture market by creating the first low-code/no-code IDP platform.
- Our Machine Learning, Natural Language Processing, Computer Vision Technologies, and a marketplace built with AI, can transform any document in any process;
- Top Analyst firms recognize ABBYY's market leadership, including Gartner, Everest PEAK Matrix ® Assessment, ISG Intelligent Automation Lens, and NelsonHall, amongst others.
ABBYY is an Equal Employment Opportunity employer that values the strength that diversity brings to the workplace. To learn more about our commitment to Diversity and Inclusion, check out the careers section on our website.
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