Staff Software Engineer (AI/ML/ SaaS)

<div class="content-intro"><p class="cgHgbA nxe3IQ Xp24Nw PanoWQ"><span class="OYPEnA">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.</span></p> <p class="cgHgbA nxe3IQ Xp24Nw"><span class="OYPEnA">Our commitment to respect, transparency, and simplicity means you can trust us to always choose to do the right thing.</span></p> <p class="cgHgbA nxe3IQ Xp24Nw"><span class="OYPEnA">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. </span><span class="OYPEnA">Over 10,000 customers trust ABBYY, </span><span class="OYPEnA">including many Fortune 500 ones. You will work on further developing a portfolio already containing client names such as </span><span class="OYPEnA">DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK.</span></p></div><p><strong>About the role:</strong></p> <p>We are looking for a Staff Software Engineer to help build and scale ABBYY’s AI platform. This role sits at the intersection of platform engineering, MLOps, and DevOps. You will own how AI services are built, deployed, observed, and evolved in production, with a strong focus on Kubernetes, cloud infrastructure, and ML lifecycle automation.</p> <p>This is a hands-on technical leadership role. You will design systems, write production code, influence architecture, and mentor engineers.</p> <p><strong><span data-contrast="auto">Job Responsibilities:</span></strong><span data-ccp-props="{}"> </span></p> <ul> <li>Design and build scalable AI platform services using Python and microservice architectures</li> <li>Own DevOps and MLOps workflows including CI/CD, model deployment, versioning, and rollback</li> <li>Build and maintain Kubernetes-based platforms for AI workloads</li> <li>Work on data pipelines, dataset versioning, and auto-labeling workflows for model training</li> <li>Enable end-to-end ML lifecycle: data ingestion, training, evaluation, deployment, and monitoring</li> <li>Collaborate closely with ML researchers, product teams, and other platform engineers</li> <li>Drive best practices in software design, reliability, security, and observability</li> <li>Lead technical discussions, review designs, and mentor team members</li> </ul> <p>&