AI Engineer - Remote

Role overview Develop practical AI features and applications for business use, working with software, data, cloud, quality and consulting colleagues. The work spans generative AI and language model integrations, agent workflows, retrieval systems and document-focused applications. Engineers are expected to experiment with emerging approaches while keeping solutions secure, reliable, scalable and useful in real settings.

Responsibilities - Integrate language models and generative AI services into applications and backend systems. - Build agent workflows, automation, retrieval-augmented generation pipelines, semantic search and document processing. - Work with embeddings, vector databases, structured and unstructured data, and model orchestration. - Prototype use cases and compare models, APIs and implementation options. - Add safeguards, validation and error handling; assess output quality, consistency and reliability. - Deploy, support and tune cloud-based AI applications, including their latency and operating cost. - Coordinate with development and QA teams, document implementations and contribute to technical direction.

Requirements - Degree in computer science, AI, data science, software engineering or a related field, or equivalent practical experience. - Software development fundamentals and familiarity with integrating applications, APIs and data. - Strong analytical and problem-solving skills, including the ability to assess AI output quality. - Clear written and verbal communication, and the ability to work independently as well as with a distributed team. - Interest in researching and learning fast-changing AI technologies.

Nice to have - Exposure to Docker, Git-based development workflows, AWS or Azure. - Experience with data preparation, processing or evaluation.

Benefits and work setup - Fully remote work with exposure to real-world, including enterprise and international client, projects. - Hands-on work with language models, agents, retrieval systems, multiple APIs and cloud platforms, plus mentoring and professional development. - Opportunities to take on more technical ownership over time and progress toward senior engineering, technical leadership, consulting, machine learning or AI architecture roles.