Applied AI R&D Engineer

About Testsigma Testsigma is an AI-native quality intelligence platform built for AI-first engineering teams. Our agentic AI coworkers autonomously write, run, heal and analyse tests across web, mobile, API and enterprise applications, so quality keeps pace with rapid release cycles. Global enterprises including DHL, IndusInd Bank and NTUC trust Testsigma, and more than 50 million test cases have been authored on our platform. In G2's Fall 2026 Reports, Testsigma was recognised as a Leader, Momentum Leader and High Performer across AI Software Testing Tools, Test Management, Automation Testing, Software Testing and DevOps. G2 also named us one of the Top 50 Best Software Companies in India for 2026, selected from more than 3,000 companies. Join us to shape how software gets tested in the AI era The role You’ll join our AI team building the core agent layer of our platform, working directly with the founding team. The team is small, so your architecture decisions will shape the platform for years. This isn’t a role for someone who chains LLM API calls and calls it agentic. The systems you build have deterministic rule layers, multi-stage reasoning pipelines, confidence scoring when the answer is ambiguous, feedback loops that improve over time, and escalation logic that knows when to stop and ask a human. You’ll invent new approaches, then ship them to production, where real customers depend on them. What you’ll do • Build multi-agent orchestration: agents that plan, execute, correct themselves and escalate, owning the intelligence behind Atto Gen, Atto Healer, Atto Arbiter and Atto Coverage • Build context graph infrastructure: a living knowledge graph connecting requirements, DOM elements, test cases, code functions and user behaviour signals • Build Playwright-based browser crawl agents that understand a running application by meaning, not just structure • Build the agent evaluation layer that tells us whether an agent’s decision was correct, and gates every prompt and model change • Integrate LLMs at production scale: prompt engineering, structured output, context window management, fallback strategies and confidence calibration • Build feedback loops that make the system measurably smarter with every test run, healed step and confirmed bug Must-have skills • 3+ years of software engineering, including recent hands-on work building multi-agent systems in production, not prototypes or demos • Hands-on experience with LangGraph, LangChain, CrewAI, AutoGen or an equivalent orchestration framework, and the ability to explain why you chose it • Designed and queried knowledge graphs or graph databases, such as Neo4j or a graph layer on a relational system • Built systems that detect absence: not just what’s wrong, but what’s missing • Production Python that is async, typed, modular and observable • Playwright, browser-use or equivalent browser automation beyond basic scripting Good-to-have skills • Agent evaluation frameworks, such as RAGAS, custom evals or LLM-as-judge pipelines • Vector stores alongside graph databases (pgvector, Pinecone, Weaviate), and knowing when to use which • Software testing, QA workflows or developer tooling • GitHub API, Jira API or similar developer integrations • Some TypeScript or Node.js What we offer • Direct access to the founding team and real influence over product direction • The chance to be a core architect of a category-defining platform • 100% company-paid medical insurance • Daily catered lunch and free snacks