Responsibilities
- Design and deliver production-grade AI systems for stakeholders, including LLM-powered applications for strategy teams
- Develop multi-agent architectures for strategy workflows including data collection and human-in-the-loop controls
- Build retrieval and vector-based systems over external data sets
- Work within an agile framework and in cross-functional teams to align AI solutions with business goals
- Engage internal stakeholders for the successful delivery of AI solutions
- Continuously monitor and optimize AI models to improve accuracy and efficiency, ensuring solutions are scalable, reliable and well-maintained
- Document processes, models and key learnings and contribute to building internal AI capabilities
- Ensure AI models adhere to ethical standards, privacy regulations and fairness guidelines
- Collaborate with external AI companies where required
Basic qualifications
- Proficiency in Python and software engineering best practices
- Strong hands-on experience with LLMs, embeddings, RAG pipelines and vector databases
- Experience designing or implementing multi-agent systems or tool-calling frameworks
- Implementing evaluation frameworks for accuracy, coverage interpretation, decision consistency and bias
- Experience with distributed systems and microservices
- Strong understanding of data structures and algorithms
- Knowledge of API design and development
- Experience working with a multi-disciplinary team
- Excellent problem-solving skills and attention to detail
- Strong communication and collaboration skills
Preferred qualifications
- Experience developing and deploying agentic AI systems including autonomous agents and multi-agent workflows
- Hands-on expertise with Retrieval Augmented Generation (RAG) techniques and their integration with Large Language Models
- Practical experience fine-tuning, customizing or implementing Large Language Models (LLMs) such as GPT, Llama or similar
- Knowledge of ML monitoring and observability
- Contributions to open-source ML projects or libraries
- Experience with high-performance computing
- Knowledge of software design patterns
- Experience with ML deployment platforms such as KubeFlow or MLflow
- Knowledge of serverless architecture patterns
- Understanding of cloud cost optimisation for ML workloads
- Experience with infrastructure-as-code such as Terraform or CloudFormation
- Private health insurance
- EPAM Employees Stock Purchase Plan
- 100% paid sick leave
- Referral Program
- Professional certification
- Language courses
- Why Join EPAM
- WORK AND LIFE BALANCE. Enjoy more of your personal time with flexible work options, 24 working days of annual leave and paid time off for numerous public holidays.
- CONTINUOUS LEARNING CULTURE. Craft your personal Career Development Plan to align with your learning objectives. Take advantage of internal training, mentorship, sponsored certifications and LinkedIn courses.
- CLEAR AND DIFFERENT CAREER PATHS. Grow in engineering or managerial direction to become a People Manager, in-depth technical specialist, Solution Architect, or Project/Delivery Manager.
- STRONG PROFESSIONAL COMMUNITY. Join a global EPAM community of highly skilled experts and connect with them to solve challenges, exchange ideas, share expertise and make friends.
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