Working at Atlassian Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company. Be the backbone of Atlassian’s Agentic AI Integration Products : The Agentic AI Integrations team is responsible for the industry-leading Rovo MCP Server, Agent to Agent integrations as well as on the mission to catapult  Atlassian value by leveraging cutting-edge AI capabilities like Claude Skills, ChatGPT/Claude Apps etc., essentially we will be working on anything and everything with AI integrations into the Atlassian ecosystem. Knack to work on bleeding-edge AI technologies: Passionate to explore and learn  AI transformative  technologies and  quickly pivot from prototyping new initiatives to building highly-scalable enterprise-grade AI products that will be used by 1000s of developers and enterprise users. ML performance, quality, and systems acumen-ship: Experience in tuning MCP or agent-facing servers for latency, reliability, token efficiency, and tool-selection quality; including dynamic tool discovery, context and response optimization, observability, automated evals, and semantic retrieval using embeddings, vector search, hybrid ranking, and reranking.     Responsibilities • Design, build, and evolve MCP servers, tools, and agent-facing APIs with concise schemas, predictable errors, safe mutations, and clear outcome-oriented contracts. • Develop accessible, responsive, and performant React and TypeScript experiences that make agent capabilities, MCP tools, and A2A interactions easy to discover, configure, and use. • Build reusable components, design-system patterns, and frontend architecture that support consistent, scalable user experiences across AI-powered products. • Integrate GraphQL and REST APIs, SDKs, streaming responses, and real-time data into reliable, user-friendly AI workflows. • Optimize token and context efficiency through dynamic tool discovery, lazy loading, bounded responses, pagination, selective field retrieval, caching, and reduced tool-call loops. • Improve end-to-end performance and reliability across front-end clients, gateways, MCP servers, search services, and downstream product systems through observability, tracing, SLOs, and production diagnostics. • Build semantic retrieval capabilities using embeddings, chunking, vector indexes, hybrid search, metadata and permission filters, ranking, reranking, and freshness strategies. • Define and operate AI/ML quality programs with JTBD-based evaluations, benchmark datasets, groundedness and relevance metrics, hallucination and bias detection, safety testing, and human feedback. • Integrate automated evaluations into CI/CD and release gates to detect regressions across model, prompt, tool, and retrieval changes. • Implement enterprise security and partner cross-functionally to deliver maintainable, well-tested AI integrations, including OAuth 2.1, tenant isolation, audit logging, prompt-injection defenses, and confirmation flows for high-impact actions. Qualifications • 7+ years of software engineering experience building and operating enterprise systems, APIs, or cloud-native products. • Strong proficiency in TypeScript/JavaScript and modern front-end development with React; experience building accessible, responsive, and performant web applications. • Hands-on experience designing or integrating MCP servers, tools, agent-facing APIs, or related context and agent frameworks. • Demonstrated ability to apply distributed-systems principles, including concurrency, connection pooling, caching, retries, timeouts, backpressure, autoscaling, and load shedding. • Experience measuring and improving latency, throughput, saturation, error rates, availability, token consumption, and end-to-end task cost. • Practical experience with AI/ML evaluation and quality engineering, including benchmark design, groundedness, relevance, safety, hallucination detection, bias analysis, monitoring, and regression prevention. • Knowledge of semantic search and retrieval systems, including embeddings, vector databases or indexes, hybrid retrieval, ranking, reranking, and permission-aware filtering. • Experience integrating GraphQL, REST, JSON Schema, streaming APIs, SDKs, and event-driven systems into reliable product experiences. • Proficiency in at least one additional systems or back-end language such as Python or Go, with strong testing and API design practices. • Proven ability to lead cross-functional engineering initiatives, communicate clearly with technical and non-technical partners, and mentor other engineers. Preferred Skills • Familiarity with MCP architecture, A2A specification, and agent collaboration frameworks (e.g., MCP servers, UI clients, and adapters). • Experience with observability, vector databases, and secure model-to-model communication. • Background in enterprise integration patterns, API governance, or developer experience platforms. • Contributions to open-source AI frameworks or standards development initiatives. Why Join • Work at the forefront of AI interoperability and system design. • Influence emerging standards that define how agents and models communicate. • • Collaborate with top engineers and  research partners building the next layer of enterprise AI infrastructure.