ai engineer

Avenga provides a full spectrum of services, including business and tech advisory, enterprise solutions, CX, UX and UI design, managed services, product development, and software development. The company operates globally with over 6000 specialists.

• Research and validate ML approaches for industrial system understanding;
• Explore methods for extracting entities, components, relationships, and structure from technical documents, diagrams, specifications, and other system descriptions;
• Design and run experiments comparing computer vision, multimodal, graph-based, and representation learning approaches;
• Build research prototypes and evaluation pipelines to test feasibility;
• Analyze model outputs, failure cases, and limitations, and propose improvements;
• Collaborate with AI engineers and domain experts to translate research findings into practical project direction;
• Present findings, experiments, and validation results to technical stakeholders.

• Strong background in machine learning research, applied ML, computer vision, multimodal learning, graph ML, representation learning, or related areas;
• Experience designing and running ML experiments, benchmarking models, and interpreting results;
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow;
• Ability to work with ambiguous research questions and translate them into testable hypotheses;
• Experience reading research papers and adapting methods to practical problems;
• Strong Python skills and ability to build research prototypes;
• Comfortable working independently in an R&D or PoC environment with evolving requirements;
• Strong commercial experience with Python and modern AI/ML ecosystems;
• Nice to have: Experience with document understanding, diagram understanding, technical drawings, engineering schematics, or similar visual/structured data, experience with knowledge graphs, graph neural networks, graph embeddings, or structured relationship modeling, experience with vision transformers, VLMs, self-supervised learning, contrastive learning, or synthetic data generation, experience with industrial, scientific, simulation, robotics, manufacturing, or physical-system data, experience with multimodal data fusion, combining visual, textual, structured, or sensor-based data, familiarity with GCP/Vertex AI or other cloud-based ML environments.

• No conditions specified