Rockwell Automation Recruitment 2026
Rockwell Automation
Experience2+ Years
QualificationB.E/B.Tech/B.Sc/BCA
Key Responsibilities
- Design, develop, test, and maintain software components and services.
- Apply secure coding and security-by-design principles.
- Participate in feature design, implementation, testing, and deployment.
- Support threat modelling and security reviews.
- Assist with vulnerability remediation and risk mitigation.
- Develop automation for security, compliance, and engineering productivity.
- Contribute to cloud-native, web, desktop, or distributed applications.
- Integrate security requirements into product designs.
- Investigate and resolve defects, performance issues, and security findings.
- Participate in code reviews and design reviews.
- Maintain technical documentation, test plans, and engineering artefacts.
- Integrate security tools into CI/CD pipelines.
- Use Generative AI responsibly to improve engineering workflows.
- Stay current with software engineering, cybersecurity, and AI-driven development practices.
- Collaborate with global and cross-functional teams.
Skills & Eligibility
- Software Engineer – Product Security:
- Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Cybersecurity, Electrical Engineering, or equivalent practical experience.
- Minimum 2+ years of software development experience.
- Experience with one or more modern programming languages such as C#, C++, Java, Python, Go, or TypeScript.
- Experience working in Agile development environments.
- Experience developing software for cloud, web, desktop, or distributed systems.
- Knowledge of object-oriented design, data structures, algorithms, and design patterns.
- Experience with TypeScript, Node.js, Angular, React, GraphQL, or similar technologies.
- Experience with Windows and/or Linux platforms.
- Knowledge of REST APIs, microservices, containers, and cloud-native architectures.
- Experience with Git, GitHub, Azure DevOps, CI/CD pipelines, and automated testing.
- Knowledge of secure software development lifecycle practices.
- Understanding of common software vulnerabilities, OWASP Top 10, CWE, and secure coding.
- Familiarity with threat modelling and security risk assessments.
- Knowledge of authentication, authorization, encryption, and security protocols.
- Exposure to DevSecOps tools such as SAST, SCA, DAST, SBOM, and container-security solutions.
- Understanding of security frameworks such as IEC 62443, NIST SSDF, CRA, NIS2, or similar standards.
- Experience with Generative AI tools such as GitHub Copilot, ChatGPT, Claude, Cursor, or similar platforms.
- Understanding of AI-assisted software development, prompt engineering, code generation, automated testing, and documentation workflows.
- Exposure to AI/ML concepts, LLMs, retrieval-augmented generation (RAG), agentic workflows, or AI-enabled developer tooling.
- Ability to evaluate AI-generated outputs for quality, security, maintainability, and compliance.
- Experience developing software for cloud, web, desktop, or distributed systems.
- Knowledge of object-oriented design, data structures, algorithms, and design patterns.
- Experience with TypeScript, Node.js, Angular, React, GraphQL, or similar technologies.
- Experience with Windows and/or Linux platforms.
- Knowledge of REST APIs, microservices, containers, and cloud-native architectures.
- Experience with Git, GitHub, Azure DevOps, CI/CD pipelines, and automated testing.
- Knowledge of secure software development lifecycle practices.
- Understanding of common software vulnerabilities, OWASP Top 10, CWE, and secure coding.
- Familiarity with threat modelling and security risk assessments.
- Knowledge of authentication, authorization, encryption, and security protocols.
- Exposure to DevSecOps tools such as SAST, SCA, DAST, SBOM, and container-security solutions.
- Understanding of security frameworks such as IEC 62443, NIST SSDF, CRA, NIS2, or similar standards.
- Experience with Generative AI tools such as GitHub Copilot, ChatGPT, Claude, Cursor, or similar platforms.
- Understanding of AI-assisted software development, prompt engineering, code generation, automated testing, and documentation workflows.
- Exposure to AI/ML concepts, LLMs, retrieval-augmented generation (RAG), agentic workflows, or AI-enabled developer tooling.
- Ability to evaluate AI-generated outputs for quality, security, maintainability, and compliance.
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