Principal Software Engineer (Agentic AI, C# / .NET)

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. The company has 8 years of experience and leadership in enterprise AI, with expertise in data, analytics, cloud & DevOps, application modernization, and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

• 8+ years of hands-on software engineering experience with versatility across legacy, under-documented enterprise codebases.
• Proven practical experience leveraging LLMs and agentic coding workflows (Copilot agent mode, Cursor, Windsurf, Claude Code, or similar) for code generation, refactoring, and test review at scale.
• Strong production-grade experience in C# / .NET and/or Python.
• Strong software testing discipline focused on test quality, test-driven refactoring, and automated test suite design.
• Hands-on experience with security remediation, dependency management, and clearing CVEs in enterprise systems.
• High autonomy, pragmatic problem-solving skills, and excellent written English communication for remote collaboration.

Nice to have:

• Experience with specification-driven AI development tools such as JUXT Allium or GitHub spec-kit.
• Track record of modernizing legacy .NET ecosystems (.NET Framework to .NET 6/8) or frontend frameworks (Angular, replacing WPF/WCF surfaces).
• Practical experience designing or extending automated code-analysis and remediation CI/CD pipelines (GitHub Actions, Azure DevOps, Octopus Deploy).
• Background in financial services or other heavily regulated enterprise environments.

• Drive agentic diagnostics, vulnerability triage, and automated code assessments using Copilot agent mode, Cursor/Windsurf/Claude Code, and client-internal LLM platforms.
• Validate and autonomously remediate pre-identified security vulnerabilities (CVE triage, dependency upgrades, SAST/DAST findings) to ensure codebases pass modern deployment gates.
• Architect functional and end-to-end test suites using AI, elevating test coverage to 70%+ to reduce manual QA dependence.
• Apply spec-driven methodologies (e.g., JUXT Allium, GitHub spec-kit) to analyze codebase correctness and enroll new services into the remediation pipeline.
• Perform deep architectural analyses on flagship codebases (>0.5M LoC) to derive strategies for automated refactoring and cloud deployment via GitHub Actions.
• Collaborate closely with onsite consultants, security leads, and client stakeholders to prioritize backlogs, validate findings, and ship production-ready updates.

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