Machine Learning Engineer

• Build and run a scalable, traceable, FDA-ready data platform bridging on-prem DGX and AWS for our 2026 submission.
• Implement ingestion + QC automation starting with CT DICOM, expanding to video/C-Arm and radiology reports.
• Implement/operate distributed processing on AWS Batch (Spot) for large-scale QC + predictions + derivatives.
• Deploy ClearML for dataset versioning/lineage and experiment tracking (runs/metrics/artifacts; provenance).
• Optimize training data access using Lance (or equivalent) for fast loading and incremental updates.
• Build a PostgreSQL-backed enrichment service for metadata/labels/predictions independent of raw media (optional search via OpenSearch/text+vector).
• Integrate/operate labeling workflows (Encord preferred; alternatives acceptable), incl. RBAC/QC/audit trail + algorithmic label ingestion.
• Establish a governed clinical validation environment meeting 21 CFR Part 11 expectations (access control, audit trail/WORM, provenance) and HIPAA/PHI handling.

Senior MLOps Specialist – 3 Month Project

AI Engineer

• Побудувати внутрішні AI-рішення з нуля .
• Перетворювати бізнес-запити команд у ефективні LLM-архітектури (RAG, embeddings, vector search).
• Закласти фундамент AI-інфраструктури, яка зростатиме разом із бізнесом.

Junior AI/ML Engineer

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration.
• Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP.
• Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift.
• Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts.
• Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output.
• Review AI-generated code before it ships for correctness, security, and type safety.
• Operate what you ship: monitoring, reliability, cost, and failure modes in production.
• Contribute to shared engineering standards, playbooks, and internal tooling.

Applied AI Engineer – Ukraine

• Design and own end-to-end AI solution architectures, covering data pipelines, AI services, integrations, and infrastructure requirements.
• Define camera setups, imaging requirements, and hardware specifications for computer vision use cases.
• Coordinate installations and integrations with internal teams.
• Collaborate with Digital, IT Infrastructure, Software Engineering, and Master Data teams to ensure scalable and maintainable solutions.
• Develop and deploy AI models on edge devices where low latency or offline processing is required.
• Create and optimize conversational AI for natural language interactions and improved user experiences.
• Continuously explore and incorporate emerging AI/ML technologies to enhance the efficiency and scalability of solutions.
• Monitor model performance and conduct regular evaluations to maintain accuracy and reliability.
• Contribute to bi-weekly brainstorming and team innovation sessions to propose new ideas and solutions.

Machine Learning Engineer jobs in Ukraine

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Senior MLOps Specialist – 3 Month Project

Junior AI/ML Engineer

Applied AI Engineer – Ukraine

AI/ML Engineer