Lead Generative AI Developer with AWS
Summary
The Lead Generative AI Developer will design and build enterprise-grade GenAI solutions using Amazon Bedrock, RAG architectures, and foundation models. The role focuses on integrating AI applications with existing systems while ensuring secure and responsible AI practices.
We are looking for a Lead Generative AI Developer with AWS to design and build cutting-edge GenAI solutions using Amazon Bedrock and similar platforms. In this role, you will develop AI-powered applications, integrate foundation models with enterprise systems and ensure secure, responsible AI practices across business scenarios.
Responsibilities
- Design and build GenAI solutions using Amazon Bedrock or similar GenAI platforms
- Integration with foundation models such as Anthropic Claude and Titan
- Perform prompt engineering and optimization
- Develop RAG (Retrieval Augmented Generation) architectures
- Create AI-powered applications such as chatbots, copilots, knowledge assistants and document processing solutions
- Evaluate and fine-tune LLM-based use cases for business scenarios
- Integrate GenAI models with enterprise applications and APIs
- Implement secure AI architectures aligned with enterprise and regulatory standards
- Ensure data privacy, model governance and responsible AI practices
Requirements
- Bachelor's or master's degree in Computer Science, Engineering or related field
- 10+ years of overall experience in IT and 5+ years in cloud architecture
- Hands-on experience with AWS cloud services such as EC2, S3 and Lambda, as well as RDS, VPC and IAM
- Proven experience with Amazon Bedrock or similar GenAI platforms
- Expertise in LLMs, prompt engineering and embeddings alongside RAG frameworks
- Knowledge of programming languages such as Python, React or Node.js
- Proficiency in designing API-driven and microservices architectures
Nice to have
- Familiarity with LangChain, LlamaIndex or similar frameworks
- Knowledge of vector databases such as OpenSearch, Pinecone or FAISS
- Understanding of MLOps and model lifecycle management
- Exposure to multi-cloud environments such as Azure or OpenAI
Benefits
Opportunity to work on technical challenges that may impact across geographies
Vast opportunities for self-development: online university, knowledge sharing opportunities globally, learning opportunities through external certifications
Opportunity to share your ideas on international platforms
Sponsored Tech Talks & Hackathons
Unlimited access to LinkedIn learning solutions
Possibility to relocate to any EPAM office for short and long-term projects
Focused individual development
Benefit package:
- Health benefits
- Retirement benefits
- Paid time off
- Flexible benefits
Forums to explore beyond work passion (CSR, photography, painting, sports, etc.)