We are looking for a hands-on AI Engineer to design, build, and maintain conversational AI agents in cloud environments. The role includes enabling interactions via voice, chat, email, or APIs, connecting AI to corporate systems and external models, and implementing evaluation and feedback mechanisms to ensure continuous improvement. Strong Python and Python ML skills are required, along with experience in agentic frameworks and cloud infrastructure.
Responsibilities
- 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
Requirements
- 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
- Comfort with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn is a plus
Nice to have
- Experience deploying to cloud AI platforms (Azure OpenAI, Amazon Bedrock, GCP Vertex AI) or on-premise solutions (e.g., vLLM)
- Experience with enterprise AI platforms such as AWS AgentCore or Databricks AgentBricks or Google Agents Space, or Azure AI Foundry
- Experience with observability and monitoring tools and frameworks
Benefits
We believe that the greatest strength of the company is its people. EPAM is fully committed to help its employees to reach their full potential and achieve their professional goals through continues learning. With this in mind, we would like to introduce to you few of the many opportunities and services which we believe will help you expand your current knowledge:- Career development opportunities with a transparent career path
- Wide range of professional trainings, workshops, and mentoring programs plus unlimited access to LinkedIn Learning
- Learning resources and financial support for completing certification programs
- Christmas and Vacation bonus
- Corporate Health Insurance (basic and supplementary)
- Free yearly medical check-ups
- 25 days of vacation