AI SRE/ AI Site Reliability Engineer

Key Responsibilities

• Manage and support infrastructure powering AI/ML and Generative AI applications.
• Design and implement scalable, highly available, and secure platform solutions.
• Build automation to reduce operational toil and improve platform reliability.
• Develop and maintain Infrastructure as Code (Terraform, Helm, Ansible, CloudFormation).
• Operate Kubernetes-based environments, container platforms, and cloud services.
• Establish and monitor SLIs, SLOs, SLAs, observability dashboards, and alerting.
• Lead incident response, root cause analysis (RCA), and reliability improvement initiatives.
• Perform capacity planning, performance optimization, and cost management.
• Support GPU-based compute environments, AI model serving, and data pipelines.
• Implement disaster recovery, backup, resiliency, and security controls.
• Collaborate with engineering and security teams to deploy and operate AI services safely.
• Maintain operational documentation, runbooks, and best practices.
• Participate in on-call support and production incident management.

Required Skills & Experience

• 5+ years of experience in Site Reliability Engineering, DevOps, Infrastructure Engineering, or Platform Engineering.
• Strong programming/scripting skills in Python, Go, Java, or similar languages.
• Hands-on experience with Docker, Kubernetes, and container orchestration.
• Experience with AWS, Azure, or Google Cloud Platform (GCP).
• Strong knowledge of Infrastructure as Code (Terraform, Helm, Ansible, CloudFormation).
• Experience with Observability and Monitoring tools such as Grafana, Prometheus, Loki, ELK/EFK, Datadog, OpenTelemetry.
• Knowledge of networking fundamentals: TCP/IP, DNS, Load Balancing, Routing.
• Experience in incident management, RCA, performance tuning, and system scaling.
• Strong understanding of security, compliance, governance, and production support.
• Excellent communication and cross-functional collaboration skills.

Preferred Skills

• Experience supporting Generative AI, LLM, MLOps, ModelOps, or AI Platforms.
• Knowledge of GPU clusters, HPC environments, Slurm, Kubernetes GPU scheduling.
• Experience with Kafka, Spark, Flink, and distributed data processing frameworks.
• Familiarity with databases such as Snowflake, Redis, SQL, PostgreSQL.
• Understanding of Embeddings, Fine-Tuning, RAG, Vector Databases, Model Serving.
• Experience with Canary Deployments, Blue-Green Deployments, Chaos Engineering.
• Exposure to financial services or highly regulated environments.