Overview In this Lead AIOps Engineer role, you guide cross-functional delivery at the intersection of development, platform engineering, and AI-driven operations. You own technical workstreams, mentor a small team, and ensure solutions are scalable, reliable, and aligned with client needs. You shape AIOps platforms, integrate AI/ML into operations, and drive continuous improvement through AI-accelerated practices. This position offers hands-on coding and leadership to deliver high-impact, innovative digital solutions.
Responsibilities- Translate business needs into scalable software and platform designs across the full SDLC
- Provide architectural guidance and sign-off on approaches
- Define and enforce documentation standards and programming guidelines
- Lead design and operationalization of AIOps platforms for event correlation, anomaly detection, and remediation
- Strategize AI/ML model integration into operational workflows (predictive alerting, root-cause analysis, capacity forecasting)
- Architect RAG pipelines and Agentic AI workflows; guide engineers through implementation
- Own and enforce the AI Development Lifecycle (AIDLC) across the team
- Drive AI-accelerated development practices and tooling usage to boost velocity and quality
- Own CI/CD architecture and IaC standards; review pipeline designs before production
- Oversee containerized workloads on Kubernetes (EKS, AKS, on-prem) and runbooks
- Lead observability strategy and AI-driven monitoring insights
- Engage clients across the lifecycle: discovery, design, architecture proposals, pre-sales, governance
- Identify automation opportunities; analyze systems for policy and tooling improvements
- Stay current with tools and technologies; contribute to industry communities
- Mentor and develop engineers to raise team capability
Key requirements- AI development and adoption for development and operations
- Hands-on experience with AI-accelerated development tools (Claude, ChatGPT, Cursor, GitHub Copilot)
- AIOps platform design and implementation experience
- AI/ML model integration for Ops use cases
- RAG development and Agentic AI/workflow design
- Cloud experience (AWS or Azure preferred; GCP considered)
- DevOps tools: CI/CD (GitLab, GitHub Actions, Azure DevOps, Jenkins)
- Infrastructure as code: Terraform, CloudFormation, Bicep, Pulumi
- Container orchestration: Docker, Kubernetes (EKS/AKS) and on-prem
- Observability toolchains and monitoring
- Programming: Java/Net, Python, Go, C#, JavaScript
- Frameworks: Angular/React/Vue.js with NodeJS
- Databases: relational and NoSQL
- Back-end development and microservices
- Data/DataOps/MLOps experience
- leadership and mentoring
- client engagement
- solution design and communication
- AIOps platform design
- AI/ML model integration for operations
- RAG pipelines
