Architecture → Customer Ownership)

CAST Software Inc.•Town of North East, NY

About The Position

CAST Software is the global leader in Software Intelligence, helping enterprises understand, improve, and transform complex software systems. Their platform provides deterministic system-level insight into large codebases, which—combined with AI models and agents—enables enterprises to move from experimentation to production-grade AI outcomes in brownfield environments. The company is hiring strong engineers and architects who want to move beyond pure development into end-to-end ownership of real-world outcomes in the AI era. The role involves working directly with large enterprise codebases (often millions of lines of code), using CAST and AI tools to understand system architecture and dependencies, enable AI-assisted modernization and transformation, and ensure outputs are accurate, complete, and production-ready. This position is for someone who wants to stay deeply technical while stepping into higher-impact, customer-facing ownership.

Requirements

5–12+ years in software engineering, architecture, or modernization
Strong experience with Java, .NET, SQL, distributed systems
Strong experience with Enterprise application architectures
Comfortable working with large, complex, poorly documented systems
Strong problem-solving mindset — able to trace issues across systems
Good communication skills, with interest in becoming more customer-facing

Nice To Haves

Exposure to or strong interest in AI/GenAI for software development
Exposure to or strong interest in Code transformation and modernization

Responsibilities

Analyze large, complex codebases (.NET, Java, SQL, etc.)
Reconstruct architecture, dependencies, and data flows using CAST Imaging
Identify risks, hidden dependencies, and modernization blockers
Use tools such as VS Code, GitHub Copilot, Claude, and CAST MCP to support code transformation (.NET → Java, legacy → modern frameworks)
Validate structural and functional accuracy of AI-generated outputs
Ensure transformations preserve dependencies and integrations, architecture integrity, security and operational requirements
Ground AI outputs in deterministic system context
Identify gaps where AI-only approaches fall short (e.g., missing layers, incorrect flows)
Iterate toward deployable, production-ready outcomes
Work directly with engineering and architecture teams
Translate technical insights into clear action plans
Drive progress from initial analysis → transformation → adoption
Lead technical discussions with architects, engineering leads, and CTO organizations
Contribute to executive conversations with clear, structured insights
Gradually take on broader ownership of customer success and outcomes