Senior R&D Engineer (Computer Vision)

SoftServe is a global digital solutions company, headquartered in Austin, Texas, founded in 1993. The team contributes to innovative research and development projects at the intersection of science, engineering, and creativity, designing transformative AI solutions that impact various application domains.

• Strong experience in generative or agentic AI and deep learning with hands-on AI model development
• Proficiency in programming languages such as Python, C++, or Java/Kotlin for building AI solutions
• Practical experience with Large Language Models (LLMs) and/or Vision-Language Models (VLMs) and their real-world applications
• Hands-on knowledge of frameworks and tools such as LangChain, LangGraph, RAG, or other agentic AI frameworks
• Experience working with cloud platforms such as GCP, AWS, or Azure for AI solution development and deployment
• Solid understanding of software development processes, collaboration models, and deployment practices
• Strong analytical and problem-solving skills
• Effective communication skills in English (at least upper-intermediate level), including presenting and consulting
• Knowledge of prompting techniques (e.g., Chain-of-Thought, Tree-of-Thought) to enhance model performance (plus)
• Experience with backend frameworks such as FastAPI and building scalable APIs (plus)

• Defining the R&D roadmap for methane emission modeling
• Leading development of novel ML architectures for anomaly detection, leak localization, plume segmentation, and quantification
• Driving collaboration with CFD specialists to integrate simulation insights into machine learning pipelines
• Designing robust, scalable data ingestion and preprocessing architectures on AWS
• Creating interactive dashboards and R&D visual artifacts to demonstrate system performance and insights
• Communicating R&D progress and results to executive sponsors, clients, and presales teams
• Mentoring a team of researchers and engineers focused on long-term innovation and applied science
• Knowledge sharing