Senior ML Engineer (Knowledge Graphs)

About Osavul

Osavul is a product company building an AI-driven intelligence platform that helps governments, international organisations, enterprises, and NGOs detect and counter hybrid threats.

We work with large-scale, multilingual data from social media, news, geospatial, and other open sources, helping analysts understand actors, events, narratives, and risks across sources, locations, and time.
 

Role Overview

We’re looking for a Senior ML Engineer to work on the shared AI layer behind our intelligence products, with a focus on NLP, information extraction, and knowledge graphs.

You’ll work on turning unstructured data into structured knowledge: extracting entities and relationships, resolving them across sources, and connecting them through knowledge graphs.

The role combines ML/NLP work with production engineering. You’ll choose and evaluate the right approaches, integrate models into large-scale data pipelines, and own solutions from experimentation and technical design through to production.

Requirements

• 5+ years of professional software engineering experience.
• Upper-intermediate (B2) English or higher.
   

Engineering Skills

• Strong Python skills and experience building and maintaining production ML services.
• Experience building asynchronous, high-throughput services, including concurrent model and API calls, retries, rate limits, and failure handling.
• Hands-on experience with FastAPI and Docker.
• Experience integrating ML components into distributed batch or streaming pipelines using Celery, RabbitMQ, Apache Kafka, or similar technologies.
• Good understanding of databases, including PostgreSQL, Elasticsearch, and vector databases.
• Strong software engineering practices around testing, debugging, maintainability, and production support.

AI Skills

• 3+ years of hands-on experience in ML/NLP engineering, including taking models to production.
• Production experience with information extraction from unstructured text, including entity and relation extraction, entity resolution, and entity linking.
• Hands-on experience designing and building knowledge graphs, including ontology / graph schema design and graph databases such as Neo4j. GraphRAG experience is relevant, but should be complemented by broader knowledge graph experience.
• Strong understanding of traditional NLP, transformer architectures, and language models, with the ability to choose the right approach based on the problem and production constraints.
• Experience with deep learning frameworks such as PyTorch.
• Experience evaluating ML/NLP systems, defining relevant quality metrics, and monitoring performance in production.
   

Nice to Have

• Experience fine-tuning or self-hosting open-source models using approaches such as LoRA / PEFT, SFT, vLLM, or similar tools.
• Experience with multimodal models, computer vision, or geospatial data.
• Background in threat intelligence, OSINT, geospatial intelligence, or defence.
   

We Offer

• Real ownership over the knowledge graph direction and the opportunity to bring ideas from experimentation into production.
• Remote-first setup across Ukraine and Europe.
• Unlimited paid time off and company-wide holidays.
• Coverage for software, AI tools, and coworking.
• Annual budget for conferences, courses, and professional development.
   

Hiring Process

Recruiter interview → ML interview with a short live coding session → engineering interview → final interview with leadership → reference check and offer.