Agentic Software Engineer (Full Stack)

Specright is an innovative company specializing in Specification Management, and they are seeking an Agentic Software Engineer (Full Stack) to deliver reliable AI agent products. The role involves building agentic applications, leveraging modern coding workflows, and ensuring production readiness of the systems developed.

Responsibilities:

• Build and Ship Agentic Applications Design, develop, and deliver agentic products end-to-end - including chat-based interfaces, AI workflow tools, and internal copilots - that serve real users in production
• Leverage Agentic Coding Workflows Use modern agentic coding tools daily (e.g., Claude Code, Codex, Cursor, and similar assistants) to accelerate delivery while maintaining high code quality and engineering standards
• Design Agent Systems Architect agent internals: tool calling, multi-step orchestration, memory and state management, retrieval pipelines, permissions models, and failure recovery strategies
• Own Evals and Quality Build and maintain agent evaluation infrastructure - regression suites, scenario-based tests, golden traces, and both offline and online metrics - so you always know whether your agent got better or worse
• Implement Observability Instrument agents with production-grade observability: distributed tracing, prompt and version tracking, tool-call telemetry, and cost/latency monitoring
• Own Production Readiness Take responsibility for the full lifecycle: rollout plans, QA, guardrails, incident response, and continuous improvement of deployed agents

Requirements:

• Proven full-stack engineering ability. You've shipped production software end-to-end - not just prototypes or demos
• Hands-on experience building LLM-powered agents that work in production. You've owned at least 1–2 agentic features or products through deployment and operation
• Deep familiarity with agentic coding best practices. You understand task decomposition, agent delegation, prompt hygiene, iterative loops, and how to review and steer agentic outputs effectively
• Practical experience with agent evals and tracing. You've gone beyond model benchmarking to build evaluation systems specifically for agent behavior - including trace-based debugging and regression detection
• Solid engineering fundamentals. Testing, debugging, security basics, performance optimization, and reliability are second nature to you
• Proficiency in TypeScript/Node.js and/or Python, with experience in modern web stacks and startup-pace delivery
• Familiarity with agent tooling ecosystems: prompt/version stores, eval harnesses, tracing and telemetry platforms (OpenTelemetry, LangSmith, Datadog, Honeycomb, or similar)
• Experience with RAG architectures: vector databases, indexing strategies, chunking approaches, and retrieval evaluation
• Experience designing tool APIs for agents: structured output schemas, idempotency, rate limiting, and error handling patterns that make agents more reliable