Key Responsibilities

AI DevOps Strategy & Architecture

- - Define and execute the enterprise AI DevOps vision, roadmap, and platform strategy.
- Design scalable AI-enabled DevOps platforms that improve software delivery, operational efficiency, and developer productivity.
- Evaluate emerging AI technologies and recommend solutions that align with business and engineering objectives.
- Establish enterprise standards, governance models, and architectural principles for AI-driven DevOps platforms.

CI/CD & Platform Engineering

- - Architect enterprise CI/CD platforms supporting secure, scalable, and automated software delivery.
- Design intelligent deployment pipelines incorporating AI-assisted validation, testing, and release automation.
- Define automation strategies for developer self-service, infrastructure provisioning, and platform operations.
- Establish platform engineering standards that improve scalability, reliability, and developer experience.

AIOps & Observability

- - Design enterprise AIOps architecture for proactive monitoring, anomaly detection, incident prediction, and automated remediation.
- Define enterprise observability strategy covering metrics, logs, traces, dashboards, and operational intelligence.
- Integrate AI-driven monitoring solutions with enterprise DevOps platforms to improve operational resilience.

DevSecOps & Governance

- - Architect secure software delivery pipelines by integrating DevSecOps controls into CI/CD workflows.
- Define governance frameworks for AI-enabled DevOps environments, ensuring compliance, security, and operational consistency.




- Establish enterprise standards for AI governance, automation security, and platform reliability.

Leadership & Stakeholder Management

- - Lead and mentor DevOps and Platform Engineering teams.
- Collaborate with Enterprise Architects, AI Engineering, Security, Cloud, and Application Development teams.
- Conduct architecture reviews, technology assessments, and solution design workshops.
Drive engineering excellence, innovation, and continuous improvement initiatives.

Required Skills & Qualifications

- - 12+ years of experience in DevOps, Platform Engineering, Cloud Engineering, or Automation Engineering, with at least 4 years in an Architecture or Technical Leadership role.
- Proven experience defining enterprise DevOps and AI automation strategies.
- Strong expertise in designing enterprise CI/CD platforms and DevOps automation frameworks.
- Experience architecting AI-powered automation solutions for software delivery and platform operations.
- Hands-on experience designing and implementing Agentic AI architectures and orchestration frameworks.
- Strong understanding of Large Language Models (LLMs) and AI-assisted software engineering.
- Experience designing intelligent workflow orchestration using modern AI automation frameworks.




- Experience with AWS or Azure cloud platforms.
- Proven expertise implementing enterprise AIOps platforms for:
- Intelligent monitoring
- Anomaly detection
- Incident prediction
- Automated remediation
- Robust scripting and automation expertise using:
- Python
- Bash
- PowerShell
- Experience architecting enterprise DevOps platforms including:
- GitHub Enterprise
- Azure DevOps
- Experience integrating:
- LLM APIs
- AI automation frameworks
- Workflow orchestration platforms
- Strong understanding of:
- Observability architecture
- Monitoring
- Centralized logging
- Distributed tracing
- Alerting frameworks
Excellent stakeholder management, solution architecture, and technical leadership skills.

Valuable to Have Skills

- - Experience with LangChain, AutoGen, CrewAI, Semantic Kernel, or similar Agentic AI frameworks.
- Knowledge of Model Context Protocol (MCP) and AI agent orchestration.
- Experience with Langfuse, OpenTelemetry, Prometheus, Grafana, ELK/OpenSearch, or Datadog.
- Experience with GitHub Copilot Enterprise or other AI coding assistants.
- Knowledge of Kubernetes, Docker, Terraform, or OpenTofu.
- Experience with Policy-as-Code (OPA, Rego, Kyverno).
- Experience with AWS, Azure, or GCP cloud-native AI services.
- Familiarity with Platform Engineering, Internal Developer Platforms (IDP), and GitOps practices.
- Knowledge of AI governance, responsible AI principles, and security frameworks for AI systems.
- Relevant certifications in Cloud Architecture, Kubernetes, AI Engineering, or DevOps.

📌 DevOps Architect (India)
🏢 TechBlocks
📍 India