Job description

Job Description: Job Title: General AI SRE (Site Reliability Engineer) Location: Hybrid – Must be onsite in McLean, VA or Richmond, VA Duration: 6 Months Job Summary: We are seeking a highly motivated and experienced General AI SRE (Site Reliability Engineer) to join our AI infrastructure team. This role requires a strong background in Generative AI, Python development, and Cloud technologies (AWS or Azure). You will be responsible for building and maintaining reliable, scalable AI systems, optimizing AI model performance, and ensuring operational excellence across AI services. Key Responsibilities: Collaborate with AI/ML engineers and data scientists to deploy and maintain Gen AI models and pipelines in production. Build and manage infrastructure as code (IaC) using tools like Terraform or AWS CloudFormation. Develop robust Python scripts and microservices to support AI workloads and monitoring systems. Implement observability, monitoring, and alerting solutions to ensure uptime and performance of AI systems. Work closely with DevOps, platform, and security teams to ensure scalable and secure cloud-based operations. Proactively troubleshoot system issues and participate in on-call rotations. Maintain and improve CI/CD pipelines for AI model training and deployment. Required Qualifications: 3+ years of experience in SRE, DevOps, or Cloud Infrastructure roles. Strong hands-on experience with Generative AI frameworks or tools (e.g., OpenAI, Hugging Face Transformers, LangChain, etc.). Proficiency in Python programming, especially in the context of AI/ML and automation. Solid experience with AWS or Azure cloud services (e.g., SageMaker, Lambda, EC2, Azure ML, AKS, etc.). Familiarity with containerization and orchestration tools (Docker, Kubernetes). Deep understanding of system performance monitoring, logging, and troubleshooting. Strong problem-solving skills and ability to work in a fast-paced hybrid environment. Preferred Qualifications: Experience working with large language models (LLMs) in production environments. Familiarity with ML model lifecycle tools like MLflow, Kubeflow, or similar. Experience with security and compliance best practices in cloud-based AI systems. Knowledge of data engineering tools such as Apache Spark, Airflow, or Databricks is a plus.

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