Senior Applied AI Engineer (LLMs, Azure)

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.

• Proven experience with at least 2 LLM production projects designing and deploying prompts in real-world applications
• Excellent understanding of prompt structure and contexts
• Proficiency in Python with experience in LLM APIs, prompt engineering frameworks and data manipulation tools such as Pandas
• Experience with at least one fine-tuning or retrieval-augmented generation (RAG) pipeline implementation
• Familiarity with prompt evaluation tools and metrics such as BLEU, ROUGE and semantic similarity, as well as human-in-the-loop validation
• Understanding of LLM-related security concerns including prompt injection, output filtering, API limitations and output structuring
• Excellent communication skills and ability to translate complex document requirements into actionable prompt logic
• Strong analytical mindset with attention to both high-level design and low-level output consistency

Nice to have:

• Experience with vector databases and retrieval-augmented generation
• Exposure to tools like LangChain, LlamaIndex or similar
• Prior work with OCR, NLP or intelligent document processing (IDP) pipelines

• Design and optimize advanced prompts for document understanding and data extraction use cases
• Collaborate with business stakeholders to understand document structures, semantics and key-value relationships
• Create PoCs using Python to validate prompt effectiveness and iterate quickly
• Maintain a prompt library with a focus on quality, reusability and compliance with security and privacy guidelines
• Perform prompt validation, stress testing and failover scenario modeling for different LLMs
• Analyze LLM output quality, fine-tune prompts and propose model-specific prompt strategies
• Work with ML engineers on fine-tuning initiatives to improve model accuracy for specific document domains
• Document best practices, prompt strategies and experiment results in a structured format
• Identify and mitigate risks related to prompt injection, hallucination or other LLM-specific vulnerabilities