Site Reliability Engineer (SRE) with MLOPS and GPU support
Summary
SRE providing production support for AI/ML platform operations on GCP with GPU infrastructure: incident triage, root cause analysis, change execution, patching, monitoring, and BCP for mission-critical systems, plus automation of routine support tasks. Requires 24x7 shift/on-call availability.
Site Reliability Engineer (SRE) AI/ML Platform Operations (GCP & GPU)
Hybrid
Hyderabad
Exp: 6+ years
Mandate Skills: Production Support Engineering, SRE, GCP, AI/ML Platform Operations (MLOPS), GPU GPU support
Job Details:
Responsible for support functions and driving the execution of multiple Application/Platform support services including incident triage, root cause analysis, change evaluation-execution-validation, deployment management, BCP and risk & vulnerability management, patching, monitoring.
Provides on-call production support of Mission Critical applications and resolve issues within RTO.
Leverage diagnostic tools to maintain, troubleshoot and restore service or data to systems
Bring in engineering, operational management in various domains like AI & Cloud
Leverage AI-enabled applications, tools, Large Language Models (LLMs), Generative AI, Agentic AI frameworks, and Retrieval-Augmented Generation (RAG) architectures
Champion continuous improvement to drive stability and operational excellence
Work with SRE mindset. Building Automated solutions for daily redundant production support activities.
Ability to understand and escalate issues efficiently and appropriately. Identify system bottlenecks and opportunities for process improvements
Communicate, co-ordinate and collaborate with multiple support teams and stakeholders
Required Qualifications:
Must have 8+ Years of Production Support experience
Proven experience implementing SRE processes in support teams
Experience supporting applications using Python stack
Knowledge and understanding of technology infrastructure: data center, network, storage, and compute domains.
Hands on experience on GCP with certification
hands-on experience in configuring, managing, and optimizing GPU-based infrastructure and environments for AI/ML workloads.
Proven expertise in diagnosing and resolving capacity, performance, and scalability issues associated with AI and machine learning workloads.
Strong experience troubleshooting, supporting, and resolving production issues in Generative AI (GenAI) applications and services.
Knowledge/experience of Python/Shell scripting.
Knowledge/ experience supporting Data Science/AI/ML platforms
Strong user/admin experience with Linux/Unix command line.
Exposure supporting with big data technologies such as Hadoop, Spark, elastic search etc
Strong experience with monitoring systems such as Splunk, App Dynamics.
Candidate should have experience in serving as a liaison between technology infrastructure operations and business projects.
Excellent verbal and written communication skills.
Exceptional problem solving and analytical skills.
Willing to work in shifts (24x7 models)