Job highlights

- Bachelors degree in Computer Science,Engineering,Data Science,or a related field (or equivalent experience). . 5+ years of experience working in DevOps or Site Reliability Engineering roles.

- Experience with GitHub Actions andJenkins or similar tools for workflow automation and CI / CD. . Argocd

- Required Qualifications.

Education Experience

Job Summary

Senior Site Reliability Engineer, JLL Technologies COE. We are seeking a skilled and experienced Senior Site Reliability Engineer to join our dynamic team. As a Senior SRE, you will be responsible for leading the implementation and maintenance of reliable, scalable infrastructure and deployment pipelines using Terraform and Infrastructure as Code practices across multiple AWS Cloud accounts.

You will play a critical role in ensuring the efficiency, reliability, observability, and scalability of our systems while fostering a culture of collaboration, automation, and continuous improvement through SRE principles. This role requires understanding of how your area integrates with development teams and the ability to translate business continuity needs into technical reliability requirements, leveraging Agentic AI capabilities to enhance operational excellence.

Responsibilities

Technical Leadership Mentorship

- Act as resource for less experienced colleagues in troubleshooting and reliability best practices.

- Act as informal coach and deliver knowledge-sharing sessions on incident response, system design, and AI-enhanced operational practices.

- Lead medium projects with manageable risks and guide team members on operational procedures.

- Provide technical guidance on reliability engineering principles and AI/ML integration opportunities.

- Share expertise in automation, monitoring, and AI-driven operational improvements with team members.

AI-Enhanced System Design Implementation

- Design independently for complex infrastructure items following Infrastructure as Code (IaC) best practices using Terraform, Docker, Kubernetes, and various scripting languages.

- Evaluate others designs using reliability engineering methodologies and provide constructive feedback.

- Consider scalability, maintenance, and operational experience in all design decisions.

- Integrate AI and machine learning capabilities into infrastructure solutions for improved reliability and efficiency.

- Lead incident response processes and apply AI-assisted diagnostics to accelerate resolution.

- Design monitoring solutions that leverage AI for anomaly detection and predictive alerting.

- Implement intelligent automation using AI-driven decision support for routine operational tasks.





Reliability Engineering Operational Excellence

- Establish and monitor Service Level Objectives (SLOs), Service Level Indicators (SLIs), and error budgets to measure and improve system reliability and performance.

- Design and implement comprehensive monitoring, alerting, logging, and observability solutions using AI/ML for proactive issue identification and automated anomaly detection.

- Lead incident response efforts, conduct blameless post-mortems, and implement preventive measures to reduce Mean Time to Recovery (MTTR).

- Solve medium to high complexity infrastructure problems using technical experience, judgment from multiple sources, and AI-powered analytics.

- Take new perspectives on existing reliability solutions and identify opportunities for AI enhancement.

- Apply AI for log analysis, pattern recognition, and root cause analysis to improve incident resolution speed.

- Implement basic AIOps practices to enhance operational intelligence.

Cross-Functional Collaboration

- Collaborate with development, operations, product, data engineering, and business teams to understand requirements and implement reliability standards.

- Translate business continuity needs into technical reliability requirements.

- Apply domain expertise using AI-driven monitoring and automation to solve operational challenges.

- Work closely with development teams to integrate SRE best practices including toil reduction, capacity planning, and chaos engineering.

- Partner with data science teams to understand and operationalize ML model reliability requirements.

- Drive adoption of AI-enhanced DevOps practices within immediate operational teams.

Knowledge Management Innovation

- Develop and maintain runbooks, documentation, and knowledge bases to support operational excellence.

- Stay current with the latest trends, tools, and technologies in SRE, DevOps and AIOps fields.

- Contribute to improving system reliability and enterprise operational efficiency within established frameworks.

- Actively contribute towards enterprise-wide reusable reliability solutions incorporating AI capabilities.

- Experiment with AI-powered tools for automation, monitoring, and documentation generation.





- Share learnings about AI/ML applications in DevOps with the broader team.

Required Qualifications

Education Experience

- Bachelors degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).

- 5+ years of experience working in DevOps or Site Reliability Engineering roles.

- Proven experience in troubleshooting complex infrastructure issues and leading incident response.

- Demonstrated experience integrating AI/ML capabilities into operational workflows.

Technical Expertise

- Solid proficiency in Terraform and Infrastructure as Code principles for infrastructure provisioning and management.

- Extensive experience with Amazon Web Services (AWS), including EC2, VPC, S3, IAM, CloudWatch, CloudFormation, and familiarity with SageMaker.

- In-depth knowledge of RDS and its various database engines (e.g., MySQL, PostgreSQL, Oracle) for database provisioning, management, and performance optimization.

- Solid understanding of Kubernetes for container orchestration, management, and resilience patterns.

- Expertise in monitoring AND automation or specialist in one area with AI integration capabilities.

AIOps Expertise

- Hands-on experience with Agentic frameworks and tools (e.g., Langgraph, Langchain and openai

- Experience implementing AI-powered monitoring and observability solutions for anomaly detection and predictive alerting.

- Understanding of AIOps practices and platforms for operational intelligence.

- Proficiency in using AI for log analysis, pattern recognition, and automated diagnostics.

- Knowledge of machine learning techniques for capacity planning and performance forecasting.

- Familiarity with MLOps concepts for reliable model deployment and monitoring.

- Experience with AI-powered automation tools and intelligent alerting systems.

Continuous Learning

- Continuous learner with passion for staying updated with SRE principles, reliability patterns, DevOps trends, and

- Proactive in exploring new AI technologies and their applications in DevOps and SRE.

Security Compliance

- Familiarity with application security best practices, tools, and compliance frameworks.

- Understanding of DevSecOps practices and AI-powered security monitoring

CI/CD Workflow Automation

- Experience with GitHub Actions andJenkins or similar tools for workflow automation and CI/CD.

- Argocd

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Site Reliability Engineer (Bengaluru)
🏢 Jones Lang LaSalle (JLL)
📍 Bengaluru