Applied Research AI Engineer

As an Applied Research AI Engineer, you will help turn the latest advances in AI into practical, scalable solutions for our customers. You will sit at the intersection of research and engineering: investigating new models, evaluation methods, and system designs, then translating them into prototypes and production-ready building blocks.

We are looking for a highly motivated Applied Research AI Engineer to join our AI engineering team. In this role, you will explore state-of-the-art techniques in areas such as LLMs, retrieval systems, agentic workflows, multimodal AI, evaluation, and fine-tuning, and work closely with engineers, project managers, and customers to bring those innovations into real-world use cases.

Key Responsibilities

• Investigating the latest advancements in generative AI, machine learning, and applied research, and assessing how they can create value for our customers.
• Designing, implementing, and benchmarking AI systems such as RAG pipelines, copilots, agentic workflows, evaluation frameworks, and fine-tuned models.
• Translating research ideas, papers, and experiments into robust prototypes and production-ready components.
• Building reproducible experimentation pipelines for model evaluation, prompt optimization, dataset curation, and system comparison.
• Collaborating with AI engineers, full-stack developers, and project managers to define technical approaches and integrate research outcomes into customer solutions.
• Improving model quality, reliability, latency, and cost-efficiency through systematic experimentation and evaluation.
• Developing and maintaining containerized AI back-ends and research tooling using technologies such as Python, Docker, and FastAPI.
• Creating clear documentation and technical communication around experiments, findings, trade-offs, and implementation decisions.
• Contributing to internal best practices around evaluation-driven development, experimentation, and applied AI research.
• Sharing knowledge with the team through technical mentorship, internal demos, and research reviews.