MIOps Engineer

About InvoiceCloud

InvoiceCloudis a leading fintech company delivering secure innovative SaaS solutions in the electronic bill presentment and payment (EBPP) space. We empower organizations across utilities government and insurance to improve customer experience accelerate payments and enhance operational efficiency.

Role Overview

We are seeking a Senior AIOps & Reliability Engineer to design and build AI- and ML-driven operational intelligence systems. This role focuses on proactive reliability observability and intelligent automation across cloud-native platforms enabling early risk detection faster incident resolution and self-healing systems.

Key Responsibilities AI & Machine LearningDriven Operations Intelligence

- Design and implement ML models for anomaly detection predictive incident detection failure forecasting and root cause analysis.
- Apply AI-assisted analysis for incident summarization classification and remediation recommendations.
- Engineer data pipelines converting observability telemetry into ML-ready datasets.
- Continuously evaluate retrain and improve models using production feedback.

Shift-Left AIOps & Reliability Engineering

- Implement shift-left AIOps initiatives to surface risks early in the SDLC.
- Apply ML to code changesTerraformdiffs and deployment metadata to predict operational risk.
- Embed ML-driven risk scoring into Azure DevOps CI/CD and PR workflows.
- Partner with engineering teams tovalidateobservability-first development practices.

AI-Powered Operational Intelligence

- Design AI-driven incident summarization AI-assisted runbooks and guided remediation.
- Build human-in-the-loop decision systems for high-impact incidents.
- Balance AI ML and deterministic automation with a focus on explainability and trust.

Observability & Telemetry Engineering

- Instrument applications usingOpenTelemetry(OTEL).
- Normalize and correlate metrics logs and traces.
- Integrate telemetry pipelines with New Relic.
- Define and monitor SLIs SLOs and operational health signals.

Cloud Kubernetes & Platform Operations

- Design andoperateworkloads on Microsoft Azure.
- Manage Azure Kubernetes Service (AKS) clusters.
- Deploy 8 services using Helm.

ML-Enabled DevOps Infrastructure & Automation

- Build Azure DevOps pipelines for application infrastructure and ML deployments.
- Manage source control using Azure DevOps Repos.
- Implement Infrastructure as Code using Terraform.
- Automate workflows using Ansible.

Intelligent Automation & Self-Healing Systems

- Build closed-loop automation triggered by ML predictions.
- Reduce alert fatigue using intelligent correlation.
- Develop self-healing systems to reduce MTTR.

Required Skills & Experience

- Strong ML fundamentals including anomaly detection and time-series analysis.
- Experience applying AI/LLM systems to operational workflows.
- Hands-on Microsoft Azure and AKS experience.
-Proficiencywith Kubernetes Helm Azure DevOps Terraform and Ansible.
- Experience withOpenTelemetryand New Relic.

Nice to Have

-MLOpsor ML lifecycle management experience.
- Python for ML experimentation or AI prototyping.
- Familiarity with SRE principles.

What Success Looks Like

- Operational risksidentifiedbefore production.
- Early incident prediction through ML and AI insights.
- Reduced alert noise and faster incident resolution.
- Continuous improvement in platform reliability.

Technical Skills

Azure AKS Kubernetes 8OpenTelemetry New Relic
AIOpsMLOps Machine Learning Anomaly Detection Time-Series Analysis
LLMs Predictive Analytics Root Cause Analysis
Azure DevOps CI/CD Terraform Ansible
Python Infrastructure as Code Observability SRE
Incident Management Self-Healing Systems