TechMojo Solutions
From TechMojo Solutions's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
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. 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. 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. Nice to have 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. Benefits 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.
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Hi [Name], I just applied for the Senior ML Performance Engineer role at TechMojo Solutions. My background in [your most relevant experience] lines up well with what the team is looking for. Would you be open to a quick chat, or could you point me to the right person on the team? Thank you!
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