About the role:

ABBYY Vantage is our flagship Document AI platform – a cloud-native, microservices-based product that combines purpose-built AI, OCR, NLP, and direct LLM integration to help enterprise customers extract, classify, and act on data from any document. Vantage 3.0, our latest release, introduces native Azure OpenAI integration, enterprise-grade compliance and redaction, a real-time analytics dashboard, and a growing library of pre-configured AI skills. Over 10,000 customers trust ABBYY – including Fortune 500 companies like DHL, Johnson & Johnson, FDA, PwC, KeyBank, Spotify, and H&R Block – to automate their most complex document workflows.

We are looking for a Staff Software Engineer to serve as a senior technical leader on the Vantage platform. You will drive the design and delivery of complex, cross-cutting capabilities – spanning document AI pipelines, LLM integrations, compliance infrastructure, analytics, and platform extensibility. Beyond your own code, you will act as a technical multiplier: influencing architectural decisions, setting quality standards, and developing the engineers around you. This is a role for someone who leads through depth, judgment, and impact.

Job Responsibilities:

• Technical Leadership: Drive the technical design and delivery of significant Vantage platform capabilities, from architecture and API contracts through deployment, monitoring, and iteration.
• Quality & Standards: Define and uphold engineering standards for the Vantage codebase – covering code quality, service reliability, performance, security, and compliance – and drive their adoption across teams.
• Strategic Collaboration: Act as a senior technical voice in cross-functional planning with product management, ML engineering, cloud architecture, and engineering leadership; translate business priorities into actionable technical roadmaps.
• Platform Improvement: Lead initiatives to improve Vantage platform health at scale: service decomposition, observability, CI/CD maturity, and developer productivity.
• Operational Excellence: Own high-severity incident response for Vantage cloud services; drive post-mortems and systemic reliability improvements.
• Engineering Mentorship: Mentor Senior and Principal Engineers on the Vantage team; build a culture of technical rigor, shared ownership, and continuous learning.
• Technical Innovation: Evaluate and champion adoption of new technologies – from GenAI model integrations to cloud-native tooling – that strengthen ABBYY's Document AI differentiation.

Job Requirements:

• 10–13 years of professional software engineering experience with deep expertise in C# .NET and C++, willing to work with both languages
• Proven experience designing, implementing, and operating CI/CD pipelines for building, testing, and deploying workflows using GitHub Actions and/or Azure DevOps Pipelines
• Strong command of microservices architecture, distributed system design, and cloud-native patterns at enterprise scale
• Demonstrated ability to mentor Senior and Mid-level Engineers and partner with Engineering Managers on technical direction
• Experience leading technical design reviews and influencing architectural decisions across team and product boundaries

Desired Qualifications

• Experience in cloud, preferably using Microsoft Azure (AKS, Storage, Key Vaults, Azure SQL or equivalent)
• Track record of delivering production-grade systems that meet enterprise compliance, security, and reliability standards
• Hands-on experience designing and operating workflow-driven systems (orchestration, retries, timeouts, and state management)
• Experience integrating Machine Learning, Neural Networks or LLMs into enterprise applications and workflows
• Comfort operating across a globally distributed engineering organization spanning multiple regions and cultures

Here are some of our local benefits:

Here’s a summary of the role:   

This is a principal-level role for someone who has already operated at Staff or Principal Engineer level in a sizeable engineering organization and can point to specific platform decisions — in production today — that they own. You'll set the technical direction for our AI-enabled platform: architecting secure, scalable, serverless systems on AWS, defining the patterns and governance practices that multiple teams adopt, and guiding where AI genuinely adds value across the stack. 

Your leverage comes from the systems, architectures, and standards you put in place — not from individual output. You'll mentor senior engineers, lead org-wide design decisions, and be the person other technical leaders turn to when the hard calls need to be made. You'll still write code, but that's not where your impact is measured. 

If you've led complex platform or AI initiatives at scale, have strong opinions about AI governance in production systems, and want the scope to shape an entire platform's future, this is that role. 

Here’s a breakdown of what you’ll do (not all of it, just the important stuff): 

• Shape engineering strategy with broad organizational impact — you'll influence long-term architectural direction across multiple teams and products, not just within your own squad. 

• Drive platform evolution by identifying cross-cutting pain points and leading the design of secure, scalable, reusable solutions built on AWS and modern serverless and microservice patterns. 

• Lead architectural discussions and design reviews where the stakes are real — making clear trade-offs around performance, security, reliability, and maintainability, and getting alignment across teams with competing priorities. 

• Own AI architectural decisions end-to-end: design AI-enabled systems with built-in governance, monitoring, and regulatory readiness baked in from the start, not retrofitted. Define where AI adds genuine value and where it doesn't. 

• Act as the organization's AI thought leader — educate engineering teams on model behavior, agentic systems, and responsible AI practices, and raise the overall maturity of how we design and deploy AI. 

• Mentor and stretch senior and staff engineers, building technical leadership depth across the organisation and holding a high bar for engineering standards. 

These are the essentials you’ll need to get an interview: 

• Principal-level track record at scale. 10+ years of software engineering experience, including at least 3 years' operating at Staff or Principal Engineer level in an organization of 100+ engineers. You've led large, complex platform or AI initiatives where you were the decision-maker, not a contributor. 

• Deep, opinionated AWS platform expertise. You've designed multi-account AWS architectures and made the call on when serverless is the wrong choice. You work fluently with Infrastructure as Code — Terraform or CDK preferred — and have strong views on observability, resilience, and security that you've translated into org-wide patterns. 

• Production AI systems experience. You've shipped AI-enabled systems — RAG pipelines, agentic frameworks, LLM orchestration, or similar — into production and can discuss the architectural decisions, failure modes, and trade-offs involved. "Understanding AI concepts" is not enough; we need someone who has built and run these systems. 

• AI governance in practice. You've defined and implemented AI governance in real production environments — bias and privacy checks in pipelines, audit-ready monitoring, AI usage policies — not just read about it. This is essential, not a bonus. 

• API design and backend platform delivery. Proven track record designing and shipping RESTful APIs and backend platform components using Node.js/TypeScript. You understand frontend concerns well enough to set API contracts that serve product teams effectively, even if you're not writing React day-to-day. 

• Org-level communication and influence. You can align stakeholders on complex technical decisions, run architecture forums, and mentor other senior technical leaders. You're as comfortable in a board-level conversation as you are in a design review. 

It would be great if you had these to, but we’ll support you if you don’t: 

• Experience building platform components or SDKs that multiple product teams depend on, including the documentation and enablement work that makes them adoptable. 

• Involvement in architecture forums, tech councils, or similar bodies that define org-wide standards — either running them or playing a leading role. 

• Experience optimising cost and performance for data and AI-heavy workloads at scale, including capacity planning and infrastructure tuning. 

• Leadership of major modernisation efforts — legacy migration to serverless, multi-region architecture, or platform consolidation across product lines. 

• A public technical presence — conference talks, open-source contributions, published writing — that demonstrates thought leadership beyond your current organisation. 
  
  #LI-Hybrid

Here’s a summary of the role:   

This is a principal-level role for someone who has already operated at Staff or Principal Engineer level in a sizeable engineering organization and can point to specific platform decisions — in production today — that they own. You'll set the technical direction for our AI-enabled platform: architecting secure, scalable, serverless systems on AWS, defining the patterns and governance practices that multiple teams adopt, and guiding where AI genuinely adds value across the stack. 

Your leverage comes from the systems, architectures, and standards you put in place — not from individual output. You'll mentor senior engineers, lead org-wide design decisions, and be the person other technical leaders turn to when the hard calls need to be made. You'll still write code, but that's not where your impact is measured. 

If you've led complex platform or AI initiatives at scale, have strong opinions about AI governance in production systems, and want the scope to shape an entire platform's future, this is that role. 

Here’s a breakdown of what you’ll do (not all of it, just the important stuff): 

• Shape engineering strategy with broad organizational impact — you'll influence long-term architectural direction across multiple teams and products, not just within your own squad. 

• Drive platform evolution by identifying cross-cutting pain points and leading the design of secure, scalable, reusable solutions built on AWS and modern serverless and microservice patterns. 

• Lead architectural discussions and design reviews where the stakes are real — making clear trade-offs around performance, security, reliability, and maintainability, and getting alignment across teams with competing priorities. 

• Own AI architectural decisions end-to-end: design AI-enabled systems with built-in governance, monitoring, and regulatory readiness baked in from the start, not retrofitted. Define where AI adds genuine value and where it doesn't. 

• Act as the organization's AI thought leader — educate engineering teams on model behavior, agentic systems, and responsible AI practices, and raise the overall maturity of how we design and deploy AI. 

• Mentor and stretch senior and staff engineers, building technical leadership depth across the organisation and holding a high bar for engineering standards. 

These are the essentials you’ll need to get an interview: 

• Principal-level track record at scale. 10+ years of software engineering experience, including at least 3 years' operating at Staff or Principal Engineer level in an organization of 100+ engineers. You've led large, complex platform or AI initiatives where you were the decision-maker, not a contributor. 

• Deep, opinionated AWS platform expertise. You've designed multi-account AWS architectures and made the call on when serverless is the wrong choice. You work fluently with Infrastructure as Code — Terraform or CDK preferred — and have strong views on observability, resilience, and security that you've translated into org-wide patterns. 

• Production AI systems experience. You've shipped AI-enabled systems — RAG pipelines, agentic frameworks, LLM orchestration, or similar — into production and can discuss the architectural decisions, failure modes, and trade-offs involved. "Understanding AI concepts" is not enough; we need someone who has built and run these systems. 

• AI governance in practice. You've defined and implemented AI governance in real production environments — bias and privacy checks in pipelines, audit-ready monitoring, AI usage policies — not just read about it. This is essential, not a bonus. 

• API design and backend platform delivery. Proven track record designing and shipping RESTful APIs and backend platform components using Node.js/TypeScript. You understand frontend concerns well enough to set API contracts that serve product teams effectively, even if you're not writing React day-to-day. 

• Org-level communication and influence. You can align stakeholders on complex technical decisions, run architecture forums, and mentor other senior technical leaders. You're as comfortable in a board-level conversation as you are in a design review. 

It would be great if you had these to, but we’ll support you if you don’t: 

• Experience building platform components or SDKs that multiple product teams depend on, including the documentation and enablement work that makes them adoptable. 

• Involvement in architecture forums, tech councils, or similar bodies that define org-wide standards — either running them or playing a leading role. 

• Experience optimising cost and performance for data and AI-heavy workloads at scale, including capacity planning and infrastructure tuning. 

• Leadership of major modernisation efforts — legacy migration to serverless, multi-region architecture, or platform consolidation across product lines. 

• A public technical presence — conference talks, open-source contributions, published writing — that demonstrates thought leadership beyond your current organisation. 
  
  #LI-Hybrid
  #LI-Hybrid