Lead Site Reliability Engineer

EPAM Systems, Inc.
United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Languages
English

Tech stack

Artificial Intelligence Amazon Web Services Microsoft Azure Cloud Computing Configuration Management Continuous Delivery Continuous Integration Cursor DevOps Design of User Interfaces Monitoring of Systems Human-Computer Interaction
+19 more
Python (Programming Language) Machine Learning Reliability Engineering Site Reliability Engineering Practices Software Engineering Scripting Google Cloud Retrieval-Augmented Generation Large Language Models Claude Code Google Vertex AI Reliability of Systems AI Coding Agents Agentic-AI Kubernetes Information Technology Azure AI AIOps Docker

Job description

In this critical role, you will collaborate closely with software developers and operations teams to ensure high reliability, scalability, and efficiency of our systems, with a strong focus on meeting and exceeding customer expectations. Your expertise will be crucial in deploying, maintaining, and automating our infrastructure and application environments to ensure seamless user experiences.

Your proactive involvement will be key to enhancing system reliability, optimizing resource utilization, and ensuring continuous improvement in our operational practices.

You will have the opportunity to lead the adoption of AI-enabled platform capabilities, such as generative AI (GenAI), autonomous agents, and AIOps, driving innovation and operational excellence.

Your responsibilities will include defining and tracking Service Level Objectives (SLOs), managing error budgets, and reducing toil through automation. You will play a pivotal role in driving the success of technology initiatives, maximizing their impact across the organization, and ensuring that solutions consistently meet the high standards our customers expect.

Responsibilities

  • Collaborate with development, security, quality, and operation teams to implement SRE practices and ensure system reliability
  • Define and support required level of reliability, availability, and performance for services and applications
  • Design and deliver Cloud-based solutions tailored to client needs
  • Troubleshoot, mitigate, and support fixing of the infrastructure and application issues in a timely manner
  • Implement a monitoring system for the infrastructure and application reliability
  • Guide adoption of AI technologies on the platform (GenAI, AI agents, AIOps) to improve operations
  • Communicate technical concepts clearly to both engineering teams and management stakeholders

Requirements

  • Bachelor’s degree in Computer Science, Engineering, or a related field
  • 5+ years of hands-on experience in Site Reliability Engineering or related roles
  • Proven experience in any cloud (AWS/GCP/Azure)
  • Experience with implementing SRE practices such as SLO/SLI, Error budgets, Postmortems, Reducing Toil, capacity planning, and Incident Management
  • Python or other scripting/programming language
  • Strong background in monitoring tools
  • Proficiency in CI/CD tools, infrastructure as code, and configuration management
  • Solid knowledge of container orchestration technologies (Kubernetes, Docker)
  • English language proficiency at an Upper-Intermediate level (B2) or higher

Nice to have

  • Certification in Kubernetes, AWS/GCP/Azure, or similar technologies
  • Proven experience in DevOps
  • Expertise in deployment and management of LLMs, including technologies like RAG
  • Knowledge of managing and optimizing AI/ML models in production environments, including basic deployment, monitoring, and maintenance
  • Native AI cloud services: AWS Bedrock, Google Vertex AI, Azure AI
  • Experience in designing, building, and operating AI agents and agentic frameworks
  • Coding Agents: Claude Code, OpenCode, Cursor, Trae, Antigravity

Skills: Amazon Web Services (AWS), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Automation, Budget Management, Budgeting, Business Operations, Capacity Management, Cloud Computing, Communication Skills, Computer Science, Configuration Management, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Docker, English Language, GCP (Good Clinical Practices), High Reliability, Identify Issues, Incident Management, Leadership, Microsoft Windows Azure, Operational Improvement, Production Systems, Python Programming/Scripting Language, Reliability Engineering, Resource Utilization, Scripting (Scripting Languages), Software Development, Systems Reliability, Time Management, User Interface/Experience (UI/UX)

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