We are seeking an experienced Lead Platform Engineer to join our Automation Engineering team.
The ideal candidate will excel in cloud infrastructure automation, generative AI, and machine learning, with a strong foundation in DevOps practices and modern scripting tools. This role involves designing cutting-edge AI-driven solutions for AIOps while innovating cloud automation processes to optimize operational efficiency.
Responsibilities
- Design and develop automated workflows for cloud infrastructure provisioning using IaC tools like Terraform
- Build frameworks to support deployment, configuration, and management across diverse cloud environments
- Develop and manage service catalog components, ensuring integration with platforms like Backstage
- Implement GenAI models to enhance service catalog functionality and code quality across automation pipelines
- Design and implement CI/CD pipelines and maintain CI pipeline code for cloud automation use cases
- Write scripts to support cloud deployment orchestration using Python, Bash, or other scripting languages
- Design and deploy generative AI models for AIOps applications such as anomaly detection and predictive maintenance
- Work with frameworks like LangChain or cloud platforms such as Bedrock, Vertex AI, and Azure AI to deploy RAG workflows
- Build and optimize vector databases and document sources using tools like OpenSearch, Amazon Kendra, or equivalent solutions
- Prepare and label data for generative AI models, ensuring scalability and integrity
- Create agentic workflows using frameworks like Langraph or cloud GenAI platforms such as Bedrock Agents
- Integrate generative AI models with operational systems and AIOps platforms for enhanced automation
- Evaluate AI model performance and ensure continuous optimization over time
- Develop and maintain MLOps pipelines to monitor and mitigate model decay
- Collaborate with cross-functional teams to drive innovation and improve cloud automation processes
- Research and recommend new tools and best practices to enhance operational efficiency
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 7+ years of experience in cloud infrastructure automation, scripting, and DevOps
- Strong proficiency in IaC tools like Terraform, CloudFormation, or similar
- Expertise in Python, cloud AI frameworks such as LangChain, and generative AI workflows
- Demonstrated background in developing and deploying AI models such as RAG or transformers
- Proficiency in building vector databases and document sources using solutions like OpenSearch or Amazon Kendra
- Competency in preparing and labeling datasets for AI models and optimizing data inputs
- Familiarity with cloud platforms including AWS, Google Cloud, or Azure
- Capability to implement MLOps pipelines and monitor AI system performance
Nice to have
- Knowledge of agentic architectures such as React and flow engineering techniques
- Background in using Bedrock Agents or Langraph for workflow creation
- Understanding of integrating generative AI into legacy or complex operational systems
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.)