Ai Lead

Location Chandigarh On-site Experience 5-7 years AI ML DevOps Observability Employment Type Full-time About the Role We are looking for a highly skilled AIOps Engineer to design and implement AI-driven operational systems and intelligent observability platforms You will work across AIOps MLOps DevOps SRE and LLMOps building self-healing infrastructure components AI-based diagnostics and autonomous remediation workflows You ll contribute to architecture d ecisions build production systems and collaborate with cross-functional engineering teams Key Responsibilities Design and implement AIOps pipelines for telemetry ingestion analytics and alerting Build AI-driven observability capabilities for anomaly detection and incident diagnostics Develop ML LLM workflows using Ray PyTorch Lightning vLLM or SGLang Implement automation for anomaly detection event correlation predictive maintenance Build and enhance self-healing infrastructure and auto-remediation runbooks Optimize model serving and LLM inference using vLLM Ray Serve Triton Kubernetes Implement real-time streaming pipelines using Kafka Spark or Flink Integrate CI CD for AI workflows with MLflow Kubeflow or Airflow Work closely with SRE platform and AI engineering teams Contribute to AIOps solution evaluation and PoCs for enterprise platforms Participate in architecture discussions design reviews and performance optimization Required Skills Qualifications Bachelor s or Master s in Computer Science Engineering or related field 5-7 years experience across DevOps SRE or AI ML infrastructure Strong programming skills in Python preferred Go or Bash Solid experience with Docker Kubernetes and public cloud AWS GCP Azure Experience with Infrastructure-as-Code Terraform Helm Pulumi Hands-on experience with distributed compute Ray PyTorch Lightning vLLM SGLang Strong knowledge of observability tools Prometheus Grafana ELK OpenSearch OpenTelemetry Splunk Datadog Experience with MLOps LLMOps tooling MLflow Kubeflow Airflow Argo Experience with messaging streaming systems Kafka RabbitMQ AWS SQS Understanding of AI-powered automation and root-cause analysis Preferred Nice to Have Experience deploying vLLM Triton or Ray Serve in production Exposure to agentic AI frameworks LangGraph AutoGen CrewAI LangChain Hands-on exposure to SGLang for LLM orchestration Familiarity with vector databases Milieus Weaviate Pine cone and RAG-based observability Experience with model monitoring drift detection and cost optimization Contributions to open-source AIOps or observability projects What We Offer Opportunity to work on next-generation autonomous operations platforms Hands-on exposure to Ray vLLM SGLang Triton PyTorch Lightning LangGraph Cross-functional collaboration across AI cloud and platform engineering Competitive compensation and strong growth path toward AIOps Lead Architect roles Requirements Key Responsibilities Design and implement AIOps pipelines for telemetry ingestion analytics and alerting Build AI-driven observability capabilities for anomaly detection and incident diagnostics Develop ML LLM workflows using Ray PyTorch Lightning vLLM or SGLang Implement automation for anomaly detection event correlation predictive maintenance Build and enhance self-healing infrastructure and auto-remediation runbooks Optimize model serving and LLM inference using vLLM Ray Serve Triton Kubernetes Implement real-time streaming pipelines using Kafka Spark or Flink Integrate CI CD for AI workflows with MLflow Kubeflow or Airflow Work closely with SRE platform and AI engineering teams Contribute to AIOps solution evaluation and PoCs for enterprise platforms Participate in architecture discussions design reviews and performance optimizatio Benefits What We Offer Opportunity to work on next-generation autonomous operations platforms Hands-on exposure to Ray vLLM SGLang Triton PyTorch Lightning LangGraph Cross-functional collaboration across AI cloud and platform engineering Competitive compensation and strong growth path toward AIOps Lead Architect roles .