Artificial Intelligence Engineer

AI Engineer

As an AI Engineer, you will play a critical role in building and scaling AI-powered capabilities that directly influence Epsilon's products, platforms, and client outcomes. You will work alongside product managers, platform engineers, and data teams to translate complex business problems into production-ready AI systems. This role offers the opportunity to work on high-impact initiatives including generative AI, agentic workflows, and intelligent automation, while contributing to Epsilon's broader goals of innovation, operational excellence, and data-driven decision making.

Design, develop, and deploy machine learning and AI models that are reliable, scalable, and production-ready. Contribute to the development of generative AI and LLM-based solutions using techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning. Build and operate AI systems end-to-end, from experimentation and evaluation through deployment, monitoring, and iteration. Collaborate with cross-functional partners to embed AI capabilities into customer-facing products and internal platforms. Improve model performance, cost efficiency, and reliability through experimentation, tuning, and observability. Help establish and evolve best practices for AI engineering, MLOps, and responsible AI within the organization. Grow technically through exposure to modern AI frameworks, cloud-based ML platforms, and real-world enterprise use cases.

4+ years of experience building machine learning or AI-driven systems in real-world production environments. Strong foundations in machine learning, statistics, data structures, and software engineering. Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX. Proficiency in Python and experience applying production-grade engineering practices. Familiarity with cloud environments (AWS, Azure, or GCP) and deploying services at scale. Ability to work across the full AI lifecycle—from data exploration and modeling to deployment and monitoring. Strong problem-solving skills and comfort operating in ambiguous or evolving problem spaces. Experience working with large language models (LLMs), generative AI systems, or agent-based architectures. Exposure to RAG pipelines, evaluation frameworks, or fine-tuning foundation models. Familiarity with MLOps tooling, CI/CD pipelines, model monitoring, or feature stores. Experience collaborating closely with product and platform teams on customer-facing AI features. MS degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (or equivalent practical experience).