Agentic Software Engineer

We are looking for Agentic Software Engineer who can combine strong software engineering fundamentals with expert use of GenAI and agentic development tools to build, test, modernize, and operate software at higher velocity and quality. This role requires hands-on engineering depth in coding, system design, testing, debugging, DevOps, security, and production support. In addition, the engineer must be highly skilled at using AI coding assistants, autonomous agents, prompt-driven development workflows, test automation generation, code analysis, documentation automation, and AI-enabled refactoring.

The ideal candidate is not just a user of AI tools, but someone who can structure work so that AI can meaningfully accelerate delivery while maintaining engineering rigor.

Key Responsibilities Design, develop, test, deploy, and maintain scalable, secure, and reliable software applications. Write clean, modular, maintainable, and well-tested code. Apply strong computer science fundamentals including data structures, algorithms, distributed systems, APIs, databases, cloud-native design, and secure coding practices. Participate in architecture, design reviews, code reviews, production support, and continuous improvement.

Agentic Engineering Workflow

Use agentic development tools to execute multi-step engineering tasks such as feature implementation, defect remediation, code migration, unit test creation, API integration, and documentation updates. Define guardrails for AI-generated work, including coding standards, security constraints, architectural patterns, testing expectations, and compliance requirements.

Measure and improve AI-assisted productivity, including cycle time, defect rate, test coverage, code review quality, and deployment frequency.

Quality, Security, and Governance

Ensure AI-generated code meets enterprise standards for security, performance, observability, maintainability, and compliance.

Identify hallucinations, insecure patterns, flawed assumptions, dependency risks, and incomplete implementations in AI-generated output.

Apply secure software development practices, including threat modeling, secrets management, dependency scanning, static analysis, and secure API design.

Strong proficiency in one or more programming languages such as Java, Python, JavaScript/TypeScript, Go, C#, or Kotlin.

Strong understanding of software engineering fundamentals, including:

• Object-oriented design
• API design
• Data structures and algorithms
• Databases and SQL/NoSQL
• Distributed systems
• Cloud-native development
• CI/CD
• Automated testing
• Secure coding

Hands-on experience with GenAI coding tools such as GitHub Copilot, Cursor, Claude Code, OpenAI Codex-style tools, Devin, Amazon Q Developer, Google Gemini Code Assist, or similar platforms.

Experience using AI to generate, test, refactor, explain, or modernize code.

Ability to critically evaluate AI-generated output instead of accepting it blindly.

Experience with unit testing, integration testing, test automation, and code quality tools.

Strong communication skills and ability to translate business needs into technical implementation.