Senior AI Engineer

EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

• Strong background in Python, mainly for hooking up AI models, automating stuff, and handling orchestration logic
• 1–2 years of hands-on experience with Generative AI and large language models (LLMs)
• Practical experience in machine learning engineering: data preparation, working with vector databases in the cloud, and training/optimizing models
• Strong backend and frontend architecture experience
• Deep understanding of ML evaluation, tuning, and prompt engineering
• Experience working with cloud platforms such as AWS, GCP, or Azure, including container orchestration and CI/CD pipelines
• Experience with agentic frameworks (LangChain, LangGraph) and multi-agent system design

Nice to have:

• Comfort with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn

• Develop and launch scalable conversational AI systems that operate across multiple channels, including chat, voice, email, and APIs
• Architect microservices and asynchronous systems for AI applications, ensuring high availability and performance
• Use Python to implement orchestration and decision logic — the control layer that manages tools, workflows, and system interactions
• Optimize AI models through fine-tuning and LoRA integration, making data-driven decisions between different approaches (FT vs RAG)
• Establish prompt engineering libraries and define evaluation protocols for prompt performance
• Design monitoring and evaluation frameworks, including dashboards, metrics, and human-in-the-loop feedback loops to maintain model quality and trust
• Work with data specialists to design pipelines for collecting, cleaning, and structuring information for AI agents
• Design CI/CD pipelines, container standards, and cloud strategies for infrastructure migrations, cost optimization, and high availability