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Project Sophia is a new generation business application, built ground up from market-disruptive, AI-first product principles. The product has been designed to completely re-define how complex, cross domain business problems are solved. Every interaction, every component and every user experience has been designed leveraging the full power and potential of generative AI and is taking the notion of AI based experiences way beyond the standard AI chat experiences. Multi-modal concepts, combined with an emphasis of visual appeal and ease of use, is empowering any business user to interact with an infinite, AI Powered Business Research canvas where users can explore and resolve any business question both within and across business domains. Powered by large language models and deep business domain expertise, Project Sophia will empower the user to build out research journeys, automatically suggesting relevant data sources and correlating insights, intelligently visualizing exploration outcomes and suggesting next actions in the research journey. Project Sophia represents the new way of doing business, enabling strategic business decision making in a fraction of the time – filling a gap in the market where no app exists today. As we advance our product journey, we are innovating and iterating at a rapid pace. To further accelerate our momentum by bringing agentic, AI-powered experiences to the business application space and transforming how people work we continue to expand our investments in generative AI capabilities.
Job Responsibility:
Design, implement, and ship AI-first product capabilities end-to-end from rapid prototype to production, spanning LLM-powered services, retrieval/grounding pipelines, and intelligent UX experiences that delight users through Sophia’s AI canvas
Own architecture and implementation across the full stack integrating front-end experiences, back-end services, and AI orchestration layers that connect models, context, and tools to deliver cohesive, extensible, high-performance systems
Collaborate with design, research, and platform teams to adapt or fine-tune LLMs/SLMs and multimodal models for real-world customer scenarios, ensuring outcomes are contextual, transparent, and human-centered
Build agentic, tool-using, and multimodal workflows that reason across data and services
optimize for safety, latency, reliability, and cost efficiency
Contribute to engineering excellence secure-by-design, accessibility compliance, automated testing, and code craftsmanship across the product lifecycle
Instrument and evaluate AI features with telemetry, experimentation, and continuous feedback loops to refine reasoning quality and user experience
Drive live-site reliability and operational excellence, participating in On-Call rotations while maintaining a sustainable, high-ownership engineering culture
Mentor peers and influence cross-team practices, sharing knowledge across AI, UX, and system-design disciplines
Requirements:
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
3+ years of extensive experience with one or more modern web technologies such as .NET / Node / React / Angular, building RESTful APIs
2+ years of solid experience in an OO Language like C# or Java
1+ year experience with large language models (LLMs) and generative AI
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
Nice to have:
5+ years of experience in software development
Excellence in one or more general programming languages including but not limited to: Python, C#
JavaScript
TypeScript
Comfortable driving complex server & client architecture across large product teams
Hands-on experience with modern LLM evaluation techniques, including LLM-as-a-Judge, agentic evaluations, and RAG assessments
A track record of delivering successful, large-scale applied ML projects in an industry setting
Experience with MLOps practices, including model versioning, automated testing, monitoring, and CI/CD for machine learning