AI Engineer

• Design and implement AI-integrated backend systems, including LLM-powered agents, RAG pipelines, tool orchestration frameworks, and insurance automation workflows.
• Own technical decisions from architecture through production, addressing not only the happy path but also observability, failure handling, data quality, scalability, and performance under real-world load.
• Actively participate in product design by identifying technical risks early, proposing pragmatic solutions, and understanding the business rationale behind every feature.
• Use AI tools effectively and critically throughout the development lifecycle, including code reviews, design documentation, test generation, and debugging, while maintaining full accountability for the resulting output.
• Maintain strong engineering practices in a fast-moving environment through structured pull requests, design documentation, test strategies, CI/CD pipelines, and meaningful code reviews.
• Build lean solutions first, then iterate and scale. Avoid overengineering and evolve the architecture as product requirements mature.
• Mentor team members and help raise the overall technical standard through knowledge sharing, code reviews, and leading by example.

Senior AI Software Engineer

Machine Learning Engineer

• Design, develop, and optimize machine learning models in one or more solutions, including asset generation and capture, render enhancement, scene intelligence, agentic design workflows, and intuitive design interactions.
• Investigate and bring techniques from a variety of AI research areas, such as diffusion, super-resolution, conditioned generation, plus neural and differentiable rendering, into artists’ hands.
• Evaluate, integrate, and orchestrate off-the-shelf third-party foundation models to accelerate feature development and deployment.
• Mentor other engineers and contribute to the growth of the team’s knowledge and expertise in machine learning.
• Collaborate with cross-functional teams and our ML Product Manager to define the product requirements and scope of delivery of solutions to product teams.
• Work closely with our MLOps Engineer to develop and maintain pipelines for distributed training, inference optimization/quantization/serving, experiment tracking, model versioning & validation, and deployment to the cloud (AWS/Azure/GCP).
• Implement appropriate model evaluation tests, data curation processes, and apply dataset-rights awareness, and responsible AI/governance.
• Stay updated and share knowledge on the latest developments in machine learning, generative AI, natural language processing, and 3D visualization, and implement cutting-edge techniques to enhance our solutions.
• Ensure high-quality code and documentation, following best practices in software development and machine learning.

• Design and build our central MLOps Platform covering the complete ML lifecycle
• Architect robust CI/CD workflows and self-service capabilities
• Partner with Data Scientists, ML Engineers, and Infrastructure teams
• Establish and evangelize MLOps best practices across the organization
• Optimize infrastructure costs and enhance observability

Senior MLOps Engineer

• Implement machine learning models and data pipelines based on guidance from senior engineers and scientists
• Assist in converting research prototypes into reliable, scalable production workflows
• Support development of data pipelines and feature engineering workflows
• Contribute to model training, evaluation, and performance tuning
• Deploy and monitor ML models in batch and near real-time environments
• Debug issues in data pipelines and model performance with support from senior team members
• Write clean, maintainable, and well-documented code
• Collaborate with software engineers to integrate ML components into production systems

Machine Learning Engineer I

• Innovate and oversee the AI capabilities powering reactive, proactive, and predictive customer support across all products and solutions.
• Analyze customer issues and risk patterns to embed predictive and preventative AI capabilities directly into the support lifecycle.
• Lead the ideation, prototyping, and scaling of AI-driven solutions from Proof of Concept (PoC) to full production.
• Collaborate closely with engineering, operations, product, and customer-facing teams to implement, optimize, and align solutions with business objectives.
• Continuously evaluate new AI tools and technologies to improve efficiency, accuracy, scalability, and data compliance.
• Act as a mentor within a network of AI and technical experts, sharing best practices, lessons learned, and driving value realization and adoption.

AI Architect

• Lead UX/UI design initiatives in close collaboration with product and engineering teams, with a strong focus on human-centered design and usability
• Use AI-powered tools (for example, Figma AI, Cursor, Claude, and other generative tools) to generate, explore, and refine design concepts, interactions, and UI components
• Create core UX deliverables, including user flows, wireframes, high-fidelity mockups, and interactive prototypes
• Translate design concepts into interactive prototypes that clearly demonstrate form, function, and behavior
• Conduct user research and usability testing to validate design decisions and iterate based on qualitative and quantitative feedback
• Define, document, and evolve information architecture and interaction patterns to improve clarity and ease of use across products
• Collaborate closely with developers to ensure design feasibility, smooth hand-offs, and high-quality implementation
• Contribute to basic front-end prototypes when needed to support exploration and feasibility discussions
• Help maintain, evolve, and scale design systems to ensure consistency across multiple products and platforms

Machine Learning Engineer jobs in Bulgaria

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Senior AI Software Engineer

Machine Learning Engineer

Senior MLOps Engineer

Machine Learning Engineer I

AI Architect

Senior UX/AI Engineer