AI Engineer

Role overview

Develop and operate machine learning and generative AI solutions that address business needs, from data preparation through deployment and ongoing monitoring. The role combines hands-on model engineering with production integration and direct collaboration with technical teams and client stakeholders.

Responsibilities - Build, train, evaluate, deploy, and monitor machine learning models across the development lifecycle. - Create generative AI systems, including retrieval-augmented applications, agent workflows, and conversational agents. - Partner with data engineering teams on pipelines, feature stores, and orchestration; integrate models into APIs, microservices, and cloud deployments. - Improve solutions for reliability, scalability, performance, and cost, and monitor for drift, bias, fairness, and other production issues. - Document experiments, data provenance, and design decisions, and communicate with clients to turn business needs into practical AI solutions.

Requirements - A bachelor’s or master’s degree in computer science, engineering, data science, or a related field. - At least three years of hands-on experience developing and deploying AI or machine learning systems. - Strong Python skills and experience with frameworks such as PyTorch, TensorFlow, scikit-learn, or modern language model tooling. - Understanding of machine learning fundamentals, statistics, algorithms, and data structures. - Experience deploying production models through APIs or microservices and supporting their monitoring. - Ability to adapt to changing priorities and explain technical results clearly.

Nice to have - Experience with vector databases, retrieval-augmented generation, agent workflows, or building and fine-tuning large language models. - Familiarity with speech technologies, computer vision, time-series analysis, multimodal AI, or MLOps platforms. - Contributions to research publications, open-source projects, or personal AI/ML projects.