DevOps Automation Engineer

Accountabilities:

DevOps Automation and Process Optimization: Identify manual, repetitive, and error-prone activities across infrastructure, development, deployment, and security operations. Design and implement automated and AI-assisted workflows to improve efficiency, reduce operational overhead, and increase engineering productivity. Measure automation outcomes, including time saved, reduced manual effort, and improved delivery reliability.
Infrastructure as Code and Cloud Management: Design, implement, and maintain cloud infrastructure using Terraform and other Infrastructure as Code technologies. Develop reusable infrastructure modules and establish effective practices for state management, environment promotion, infrastructure drift detection, and remediation. Manage and troubleshoot AWS services across computing, networking, storage, identity management, and databases while ensuring scalability, security, and resilience.
CI/CD Pipeline Development and Optimization: Own, maintain, and improve CI/CD pipelines covering builds, automated testing, security scanning, artifact management, and deployments. Develop reusable pipeline patterns and shared libraries to standardize software delivery. Integrate automated quality checks and security controls, configure SonarQube quality gates, and troubleshoot pipeline failures to improve deployment consistency and reliability.
AI-Assisted Engineering and Agentic Automation: Leverage AI coding agents, such as Claude Code, as key tools for engineering and automation implementation. Translate complex requirements into clear specifications, provide appropriate context and guardrails, and guide AI agents through development tasks. Review AI-generated code for functional correctness, security vulnerabilities, hallucinations, and architectural consistency. Design multi-step agentic workflows with human-in-the-loop validation and develop reusable AI skills, templates, configurations, and automation processes.
Responsible AI Implementation: Integrate generative AI tools with internal data sources, APIs, and enterprise systems where appropriate. Apply responsible AI practices, including prompt injection awareness, data sensitivity controls, sandboxed execution, and output validation to ensure secure and reliable implementation.
Production Reliability and Incident Management: Investigate and resolve production incidents, infrastructure failures, and CI/CD issues using systematic troubleshooting and evidence-based analysis. Conduct root cause investigations, implement preventive measures, and develop operational runbooks and troubleshooting documentation. Improve monitoring, observability, alerting, and overall system reliability.
Security and Quality Engineering: Apply secure-by-design principles across infrastructure, automation, and software delivery workflows. Integrate security scanning, code quality checks, and compliance controls into CI/CD pipelines. Collaborate with security teams to implement vulnerability management practices, secrets management, policy-as-code initiatives, and automated compliance evidence collection.
Cross-Functional Collaboration: Partner with engineering, product, security, and operations teams to identify automation opportunities and deliver effective technical solutions. Coordinate with distributed teams across India and the United States to align technical standards, operational practices, ownership, and handoffs.
Technical Documentation and Tooling Improvements: Maintain clear technical documentation covering systems, processes, and workflows. Evaluate existing technologies and recommend improvements based on reliability, usability, cost, and engineering efficiency. Plan and execute tool migrations through structured implementation stages that minimize disruption.
Continuous Improvement: Promote reusable automation solutions, maintainable code, effective testing, and consistent engineering standards. Contribute to improving infrastructure stability, deployment performance, security, and overall developer experience.
Requirements:
At least 3 years of professional experience building and supporting production SaaS applications or platforms.
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical discipline, or equivalent practical experience.
At least 2 years of hands-on experience with AWS services, including EC2, ECS/EKS, S3, IAM, VPC, RDS, and CloudWatch.
Proven production experience designing, implementing, and maintaining Infrastructure as Code using Terraform, OpenTofu, Pulumi, CloudFormation, CDK, or comparable technologies.
Strong experience managing production CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, CircleCI, Azure DevOps, or similar tools.
Solid understanding of public cloud architecture, containerization, serverless computing, event-driven architecture, and RESTful API design.
Proficiency in Python, Go, or another comparable programming language.
Strong knowledge of system design and distributed systems, including failure handling, state management, idempotency, and infrastructure reliability.
Demonstrated experience using AI coding agents for meaningful engineering and implementation tasks, with the ability to critically assess and validate generated outputs.
Practical knowledge of generative AI tools, prompt engineering, agentic workflows, and AI-assisted engineering productivity.
Understanding of AI limitations and risks, including hallucinations, context constraints, sensitive data exposure, and security vulnerabilities.
Strong troubleshooting, analytical, and problem-solving skills, particularly when debugging complex production environments and CI/CD pipelines.
Familiarity with Infrastructure as Code practices, Terraform modules, state management, and infrastructure drift remediation.
Experience with Jenkins shared libraries, reusable pipeline patterns, and automated software delivery processes is advantageous.
Knowledge of static code analysis and security tools such as SonarQube, Snyk, Semgrep, Checkmarx, or CodeQL is preferred.
Exposure to observability and monitoring technologies such as CloudWatch, Datadog, Prometheus, Grafana, or OpenTelemetry.
Understanding of security and compliance automation, secrets management, policy-as-code, and SOC 2 audit evidence collection is an advantage.
Experience with Docker, Kubernetes, and containerized application environments.
Familiarity with additional cloud platforms such as Google Cloud Platform (GCP) or Microsoft Azure is beneficial.
Experience developing internal developer platforms or self-service engineering tools is a plus.
Background in cybersecurity, vulnerability management, or security-focused SaaS products is desirable.
Excellent written communication and technical documentation skills.
Ability to collaborate effectively with distributed teams across multiple time zones and attend scheduled late-evening meetings several times per week.
Strong sense of ownership, initiative, and adaptability, with the ability to independently manage technical challenges and deliver practical solutions.
Important eligibility clarification: The original job description also includes requirements for candidates to be legally authorized to work in the United States without current or future employer-sponsored visa support, as well as to reside within commuting distance of San Jose, California, or Albuquerque, New Mexico. These conditions appear inconsistent with the advertised India-based remote location and should be clarified with the hiring team before publication.
Benefits:
Competitive annual compensation: INR 20–30 LPA, depending on experience and qualifications.
Employment type: Full-time position.
Work arrangement: Remote opportunity advertised for candidates based in India.
Technical ownership: Opportunity to take ownership of automation initiatives and influence engineering workflows across a cloud-native SaaS environment.
Advanced technology exposure: Hands-on experience with AWS, Infrastructure as Code, CI/CD automation, containerization, and AI-powered engineering tools.
AI-driven innovation: Opportunity to work with AI coding agents and develop intelligent, reusable automation workflows.
Professional development: Continuous exposure to modern DevOps practices, cloud architecture, infrastructure security, and production engineering.
Cross-functional collaboration: Work with engineering, product, security, and operations teams across India and the United States.
Career growth: Opportunity to strengthen expertise in cloud infrastructure, platform engineering, DevOps automation, and AI-assisted software delivery.
Engineering impact: Contribute directly to improving system reliability, deployment efficiency, security, and developer productivity.