Lead DevOps AI Engineer

We are looking for a Lead DevOps / AI Engineer to join our Site Reliability Engineering (SRE) team. The role combines DevOps, cloud infrastructure, automation, AIOps and MLOps to build highly reliable and scalable production environments.

Key Responsibilities:

• Design, build and manage scalable cloud infrastructure using Infrastructure as Code.
• Manage and optimize AWS, Kubernetes and Docker environments.
• Build and maintain CI/CD and MLOps pipelines for ML models and LLMs.
• Implement AI-driven anomaly detection, predictive alerting and automated incident response.
• Develop automation and self-healing workflows to improve system reliability and reduce MTTR.
• Manage observability using Datadog, Prometheus, Grafana and OpenTelemetry.
• Optimize AI/ML workloads, including GPU/CPU utilization, inference performance and cloud costs.
• Implement security, disaster recovery and reliability best practices.
• Lead technical decisions and provide guidance across DevOps, SRE, automation and MLOps.

Required Skills:

• 8+ years of experience in DevOps, SRE or Cloud Platform Engineering.
• Strong hands-on experience with AWS, Kubernetes and Docker.
• Proficiency in Terraform, Pulumi or CloudFormation.
• Strong scripting/programming skills in Python, Go or Bash.
• Experience with GitHub Actions, GitLab CI or ArgoCD.
• Hands-on experience with Prometheus, Grafana, Datadog or OpenTelemetry.
• Hands-on exposure to AI/ML deployment pipelines.

Good to Have:

• Experience with MLflow, Kubeflow, Ray, vLLM or Triton Inference Server.
• Experience deploying LLMs or Generative AI solutions in production.
• Knowledge of vector databases such as Pinecone, Milvus or Qdrant.
• Familiarity with LangChain or LlamaIndex.