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.

What You'll Achieve

• 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

Who You Are

• What You'll Bring With You
  • 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, Tensor Flow, 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
• Why You Might Stand Out From Other Talent
  • 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)