We are hiring an AI Engineer to lead the development of next-generation features for our Product Lifecycle Management (PLM) platform. This role is deeply technical, focusing on the end-to-end lifecycle of AI-powered solutions. You will be responsible for building and integrating AI/ML models using cutting-edge technologies like NLP, LLMs, and multimodal AI. Key responsibilities include model fine-tuning and optimization, as well as the implementation of monitoring and governance frameworks to mitigate risks such as prompt injection and model drift. You'll work cross-functionally with product and engineering teams to deliver innovative, domain-specific AI capabilities that transform the PLM user experience.

Intellectsoft is a software development company delivering innovative solutions since 2007. We operate across North America, Latin America, the Nordic region, the UK, and specialize in industries like Fintech, Healthcare, EdTech, Construction, Hospitality, and more, partnering with startups, mid-sized businesses, and Fortune 500 companies to drive innovation and scalability. Our clients include Jaguar Motors, Universal Pictures, Harley-Davidson, and many more where our teams are making daily impactTogether, our team delivers solutions that make a difference. Learn more at

Requirements

5+ years of practical experience in AI and machine learning development. Proficiency in building and deploying advanced AI solutions with a focus on NLP and Large Language Models (LLMs) like GPT, Claude, Gemini, BERT, and T5. Hands-on experience developing AI-driven conversational agents (, chatbots, virtual assistants) using orchestration frameworks such as LangChain, Semantic Kernel, or LangGraph. Demonstrated expertise in Retrieval-Augmented Generation (RAG) pipeline development, including prompt engineering (zero-shot, one-shot) and model fine-tuning. Working knowledge of AI-specific search technologies like Azure Cognitive Search, Elasticsearch, or Haystack. Extensive experience with the Azure AI ecosystem, including Azure AI Services, Azure OpenAI Service, and Azure Kubernetes Service (AKS). Proficiency in containerization technologies with a focus on Docker and Kubernetes. Familiarity with MLOps principles, including model versioning and continuous integration/continuous deployment (CI/CD) using tools like Azure DevOps or GitHub Actions. Proficiency in .NET (C#) and JavaScript/TypeScript. Experience developing microservices and enterprise-scale applications.

Nice to have skills

Wide expertise in backend or full-stack engineering. Demonstrated history of deploying AI solutions within enterprise software environments. Extensive knowledge of Azure for infrastructure and deployment. Strong ability to work effectively and communicate with engineering and product teams.

Responsibilities

Engineer and deploy new AI capabilities leveraging natural language processing (NLP), large language models (LLMs), and multimodal AI. Create solutions that use AI to improve how users interact with the platform, process efficiency, and decision-making Refine and improve AI models for higher precision and efficiency. Establish frameworks for monitoring, testing, and governing AI features to manage risks like prompt injection and model drift. Partner with teams to refine and scale AI/ML solutions.

Benefits

35 absence days per year for work-life balance Udemy courses of your choice English courses with native-speaker Regular soft-skills trainings Excellence Сenters meetups Online/offline team-buildings Business trips