Open-Source Observability and AIOps platforms Expert

Key Responsibilities

• Define and enforce best practices, governance frameworks, and standards for observability and AIOps implementations.
• Design and Deploy observability solutions using open-source toolsets (Prometheus, Grafana, Loki, Tempo, ELK/OpenSearch, Open Telemetry etc)
• Build scalable and resilient observability platforms across multi-cluster and multi-cloud environments; establish MELT strategies; optimize storage efficiency and query performance
• Applying AIOps methodologies and machine learning techniques to observability data for automated event correlation, anomaly detection, root cause analysis, and predictive insights
• Design and deploy RAG-based architectures to enable contextual incident triaging, intelligent knowledge retrieval, and accelerated root-cause analysis.
• Integrate Large Language Models (LLMs) with observability platforms to support incident summarization, and AI-driven recommendations.
• Leverage advanced analytics to detect performance bottlenecks in observability/AIOps platforms and implement proactive optimizations.
• Drive data governance initiatives in collaboration with cross-functional teams to ensure data quality, privacy, and security.
• Stay abreast of emerging trends in open-source observability, AIOps, and LLM technologies to drive business-aligned strategies

Mentor and upskill junior team members by developing technical content, documentation, and training materials.

Skills & Qualification

• 810 years of extensive experience in observability, monitoring, and AIOps solutions across enterprise environments
• Proven hands-on expertise in implementing open-source observability stacks (OpenTelemetry, Prometheus, Grafana, Loki, ELK/OpenSearch, etc.).
• Demonstrated experience in AIOps solution design and implementation, including noise reduction, event correlation, anomaly detection, and predictive analytics.
• Strong proficiency in containerization and orchestration technologies (Kubernetes, Docker) and cloud-native architectures.
• Experience working with major cloud platforms (AWS, Azure, GCP) for large-scale, distributed observability deployments.
• Skilled in scripting and automation using Python, Ansible, Bash, and Terraform.
• In-depth knowledge of APIs, data pipelines, and integration frameworks for telemetry ingestion and processing.
• Solid understanding of Retrieval-Augmented Generation (RAG) architectures and practical experience integrating Large Language Models (LLMs) into enterprise observability workflows..
• Excellent analytical, problem-solving, and communication skills, with the ability to collaborate effectively across cross functional teams