Principal AI Architect (Software)

Role Overview We are looking for a Principal AI Software Architect with a deep foundation in software engineering, backend systems, distributed systems, and large-scale application architecture , combined with hands-on experience building Generative AI and Agentic AI systems . This is a Software & Systems Architecture role—not a Cloud Infrastructure or Solutions Architect role . The ideal candidate has progressed from being a strong hands-on software engineer into a Staff Engineer, Principal Engineer, Software Architect, Systems Architect, or Distinguished Engineer , while continuing to design and build complex software systems. You will be responsible for architecting next-generation AI-native software systems and transforming complex, long-lived enterprise applications into architectures that can be understood, operated, and progressively modernized using AI agents. Key Responsibilities Architect and build production-grade Agentic AI systems integrated with complex enterprise software platforms. Design multi-step agent workflows involving reasoning, tool calling, orchestration, state management, context management, retries, checkpoints, and failure recovery. Enable AI agents to securely interact with APIs, databases, enterprise applications, developer tools, source-code repositories, build systems, testing frameworks, and CI/CD pipelines . Design and build MCP (Model Context Protocol) servers or equivalent tool-integration layers that allow AI agents to safely interact with enterprise systems. Apply LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK , or similar technologies where appropriate. Architect large-scale backend systems using distributed systems, microservices, event-driven architectures, messaging, APIs, and asynchronous processing . Drive modernization of complex legacy applications, including monolith decomposition, Strangler Pattern, service extraction, dependency modernization, and progressive architectural transformation . Build mechanisms that help AI agents understand large codebases, including code structure, dependencies, APIs, services, business rules, and execution flows . Explore and implement AI-assisted software engineering capabilities for code analysis, code generation, refactoring, test generation, dependency upgrades, vulnerability remediation, build execution, debugging, and automated validation . Design deterministic validation and governance mechanisms around AI-generated actions and code changes. Establish production-grade observability, evaluation, guardrails, authorization, auditability, security, and human-in-the-loop controls for Agentic AI systems. Remain hands-on with architecture, design, prototyping, and critical implementation while providing technical leadership to engineering teams. Required Experience 12+ years of software engineering experience preferred . Exceptional hands-on candidates with 12–14 years of highly relevant experience may also be considered. Strong career foundation in backend/software engineering , with hands-on expertise in one or more of: Java, Python, Golang, Node.js, C++, or similar backend technologies . Deep understanding of distributed systems, concurrency, microservices, system design, APIs, event-driven architectures, messaging, and data systems . Strong experience designing and building high-scale, high-availability, resilient production software systems . Experience modernizing complex or legacy software platforms. Hands-on experience with LLMs, Generative AI, Agentic AI, AI agents, or AI-powered automation . Experience integrating AI systems with real-world tools, APIs, databases, enterprise systems, or software-development workflows . Strong understanding of software engineering fundamentals, including SOLID principles, design patterns, testing, observability, reliability, and security . Highly Preferred Production experience building Agentic AI systems rather than only chatbots or basic RAG applications. Hands-on experience with MCP (Model Context Protocol) . Experience with LangGraph or similar agent orchestration frameworks . Experience building multi-agent or long-running agent workflows . Experience with persistent agent state, memory/context management, checkpoints, retries, and resumability. Experience implementing agent tool calling and controlled execution of state-changing actions . Experience applying AI to the Software Development Lifecycle (SDLC) . Experience building agents that interact with code repositories, builds, tests, CI/CD pipelines, developer tools, or security tooling . Experience with LLM evaluation, guardrails, agent observability, sandboxing, authorization, and human approval workflows .