Sr Subject Matter Expert (DFS)

Job Summary

We are looking for an AI Observability Architect to design and lead the transformation of monitoring and reliability operations from reactive to predictive and self-healing. The role involves architecting AI-driven observability platforms, defining ML/DL model strategy, and enabling automated detection, correlation, and remediation across IT systems.

Key Responsibilities

• Architect an end-to-end AI observability platform unifying logs, metrics, traces, and events into a single observability data store.
• Design the strategy for predictive/unsupervised ML & DL models (Isolation Forest, Autoencoders, LSTM) for anomaly detection, degradation prediction, and Root Cause Analysis (RCA).
• Define architecture for risk scoring, feature engineering pipelines, and time-window based telemetry aggregation across infra, network, and process signals.
• Build the framework for automated alerting and proactive remediation, progressing toward AI-driven and AI-led resolution with reduced human intervention.
• Define KPIs/SLOs for critical systems and translate them into monitored signals within the observability platform.
• Integrate observability insights into conversational/agentic interfaces and dashboards for natural-language querying of health, risk, and RCA.
• Establish governance, security (RBAC, Entra ID), and scalability patterns so the platform extends across multiple domains.
• Mentor and guide engineers transitioning into AI/ML-driven observability roles.

Skill Requirements

• 6+ years in observability/SRE/monitoring, with 2+ years architecting AI/ML-driven solutions.
• Strong Python skills; hands-on experience with scikit-learn, TensorFlow/PyTorch (LSTM, Autoencoders, Isolation Forest).
• Deep experience with Azure Log Analytics, KQL, and Databricks/PySpark for large-scale telemetry processing.
• Proven ability to design anomaly detection, event correlation, and automated RCA systems.
• Experience with monitoring tool ecosystems (AppDynamics, SolarWinds, Azure Monitor) and unifying them into a common data model.
• Familiarity with agentic AI concepts, Copilot Studio, or conversational interfaces for operational insights.
• Strong architectural and stakeholder communication skills, with the ability to align technical design with business outcomes (MTTD, MTTR, MTBF).

Other Requirements

• Experience building self-healing/automated remediation systems.
• Exposure to Microsoft Fabric or unified data platforms for observability data consolidation.
• Background mentoring SRE teams transitioning into AI/ML-capable roles.