Job Opening || Site Reliability Engineer SRE – ML platform || Austin, TX and Sunnyvale, CA (Onsite)
pradeep bhondwe
Hello,
We are searching for Professionals below business requirements for one of our clients. Please read through the requirements and connect with us in case it suits your profile.
- Please share profiles only with
- Strong hands-on ML Platform Experience
We are seeking an experienced Site Reliability Engineer (SRE) – ML Platform / MLOps Engineer to support the reliability, scalability, and performance of our Machine Learning platforms. This role focuses on building and operating production-grade ML systems using Kubernetes, Python, cloud infrastructure, and modern MLOps practices.
The ideal candidate has strong experience in MLOps, cloud-native architecture, containerization, and CI/CD, along with a solid understanding of ML models and Large Language Models (LLMs). You will work closely with data scientists, ML engineers, and software teams to design, deploy, and maintain robust ML pipelines and services.
Key Responsibilities
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Design, deploy, and maintain scalable ML platforms using Kubernetes, Docker, and cloud services (primarily AWS)
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Build and operate end-to-end MLOps pipelines, including model training, validation, deployment, and monitoring
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Ensure high availability, reliability, and performance of ML production systems
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Develop automation tools and services using Python
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Implement and manage CI/CD pipelines for ML and microservices workloads
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Support ML workloads involving LLMs and traditional ML models
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Collaborate with data scientists to productionize models and optimize workflows
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Administer Linux systems and troubleshoot infrastructure issues
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Design cloud-native microservices and APIs for ML applications
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Manage and integrate data stores such as MongoDB and search platforms like Apache Solr
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Implement monitoring, alerting, logging, and benchmarking for ML systems
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Translate business requirements into technical solutions
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Contribute to best practices around testing, security, and operational excellence
Required Experience
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6+ years of hands-on experience in MLOps / SRE / Platform Engineering
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Strong proficiency in Python
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Extensive experience with Kubernetes and containerized environments
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Solid knowledge of AWS (or Azure/GCP) cloud platforms
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Experience with MongoDB
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Strong Linux administration skills
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Experience with microservices architectures
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Hands-on experience with CI/CD pipelines
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Working knowledge of ML models and Large Language Models (LLMs)
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Experience productionizing ML systems built with open-source tools
Technical Skills
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Python
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Kubernetes & Docker
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AWS (or Azure/GCP)
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MongoDB
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Microservices Architecture
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Apache Solr
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MLOps frameworks (Kubeflow, MLflow, Airflow, DataRobot, Argo, etc.)
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CI/CD pipelines
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Linux system administration
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REST APIs and cloud integrations
Preferred Qualifications
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Experience with workflow orchestration tools such as Kubeflow, Airflow, or Argo
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Experience building custom integrations between cloud-based systems using APIs
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Strong understanding of software testing, benchmarking, and continuous integration
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Exposure to ML methodology and best practices
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Ability to design and implement cloud-based ML solutions
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Excellent communication skills and ability to collaborate across teams
