World Wide Technology (WWT), a 36-year-old global technology solutions provider specializing in systems integration, Infra-Cloud security, application development, AI Services, and supply chain solutions. With a workforce of 10,000+ employees and strategic partnerships with leading OEMs such as Cisco, Dell EMC, Microsoft, and NVIDIA, WWT delivers cutting-edge infrastructure, cloud, security, and custom application services to clients across 35 countries. Our Advanced Technology Centers (ATCs)—lab setup environments spanning over one million square feet of world-class integration and distribution space enable us to deliver unmatched value and innovation at scale. Recognized as one of the Best Places to Work by Glassdoor and Fortune for 14 consecutive years, WWT is also ranked #6 on India’s Great Place to Work list for 2025.
World Wide Technology Holding Co, LLC. (WWT) currently has an exciting opportunity available for the role of DevOps Engineer – AI Systems Development role at Bengaluru (AI Systems Development). If you are interested in this opportunity, please respond with an updated resume and the required details at the bottom of this email.
This is a contract Role & we use a global payroll partner to manage payroll for all contract opportunities with us. A proper background verification of past employments, criminal checks would be done for onboarding to any opportunity with WWT.
Role: DevOps/Software Engineer – AI Systems Development
Number of Positions: 2 roles
Location: Onsite, Bangalore, India (3 days WFO per week)
Long- Term Contract
Role Overview
- We are looking for a hands-on DevOps Engineer with strong experience building and supporting AI-powered systems and developer workflows.
- The role sits at the intersection of DevOps, Python development, AI/GenAI systems, and SRE practices.
- The ideal candidate should have strong practical exposure to using AI tools and frameworks to build systems, automate workflows, and solve engineering problems.
- Candidates with approximately 1+ year of hands-on experience working with AI-assisted development and AI systems will be preferred.
- This is not a traditional DevOps-only role. The candidate should be comfortable building systems using AI platforms/tools such as Claude and working with evolving AI development frameworks.
Team Mission
- Build and support AI-enabled engineering tools, workflows, and systems.
- Develop reliable and scalable solutions combining AI capabilities with DevOps and SRE practices.
- Enable engineering teams to use AI effectively for development, automation, troubleshooting, and productivity.
- Establish robust, observable, and maintainable AI-powered systems and workflows.
What You Will Do
- Design, develop, and maintain AI-enabled systems, tools, and engineering workflows.
- Use Python to build automation, APIs, services, integrations, and AI tooling.
- Work extensively with AI development platforms and tools, including Claude or similar AI coding/development environments.
- Build and integrate AI workflows involving prompts, agents, tools, plugins, MCP servers, and related components.
- Develop systems that enable AI tools to interact with APIs, services, data, and engineering environments.
- Debug and troubleshoot AI-generated code and AI-driven workflows.
- Work with SRE and DevOps teams to deploy, operate, monitor, and troubleshoot AI systems.
- Use Kubernetes and container-based environments for deployment and operations.
- Implement basic observability using tools such as Grafana and Prometheus.
- Participate in technical design, code reviews, troubleshooting, and continuous improvement.
- Translate engineering requirements into practical AI-enabled tools and solutions.
Required Qualifications
- 6–8 years of overall software/DevOps engineering experience.
- Strong hands-on Python programming experience.
- Strong practical experience building AI/GenAI-powered systems or developer tools.
- Hands-on experience using AI coding/development tools such as Claude, GitHub Copilot, or equivalent platforms.
- Experience using AI for software development, automation, debugging, or engineering workflows for approximately 1 year or more.
- Understanding of AI agents, LLM-based workflows, prompt engineering, tool calling, and AI orchestration.
- Familiarity with MCP servers, plugins, skill files, or similar AI extensibility concepts.
- Good understanding of DevOps fundamentals, including CI/CD, containers, deployment, and troubleshooting.
- Basic to intermediate Kubernetes knowledge.
- Exposure to Grafana and Prometheus or similar observability tools.
- Strong debugging and problem-solving abilities, particularly around AI-generated code and AI system failures.
- Ability to work closely with SRE, software engineering, and AI/ML teams.
- Strong communication and collaboration skills.
Preferred Qualifications
- Experience with FastAPI or similar Python web frameworks.
- Experience building AI agents, AI assistants, internal developer tools, or AI-powered automation.
- Experience integrating LLMs with APIs, databases, services, or enterprise systems.
- Experience with MCP-based architectures or similar AI tool-integration frameworks.
- Experience with Kubernetes-based AI/ML or application deployments.
- Exposure to cloud platforms and cloud-native architectures.
- Experience with monitoring, logging, alerting, and production troubleshooting.
- Experience working in an environment where AI is actively used for software engineering and system development.
Success in This Role Looks Like
- You can independently build and troubleshoot AI-enabled engineering systems.
- You are comfortable using tools such as Claude or equivalent AI platforms as part of your development workflow.
- You can combine strong Python development skills with practical DevOps and SRE knowledge.
- You can understand an AI-generated implementation, identify issues, and fix or improve the resulting code.
- You can work effectively with SRE and engineering teams to deploy and operate AI systems.
- You can quickly adapt to new AI frameworks, tools, and development patterns as the technology evolves.
Candidate Profile
- The ideal candidate is a hands-on DevOps/Software Engineer with strong AI systems development experience.
- They should not be a traditional DevOps engineer who has only basic awareness of AI. They need to have actually used AI tools to build systems, automate workflows, develop code, and solve engineering problems.
- Strong Python is important, while DevOps fundamentals, Kubernetes, observability, and SRE knowledge provide the operational foundation.
- Candidates who have been actively using Claude, Copilot, or similar AI development tools for around a year or more and can demonstrate practical outcomes from that experience should be prioritised.