Responsibilities
• Architect and build production agentic AI systems: Design and implement scalable AI agents and services, including LLM orchestration, multi-agent workflows, RAG pipelines, tool integrations, memory and state management, and human-in-the-loop experiences.
Lead through hands-on engineering: Write, review, and maintain production-quality C# and Python code. Build critical components, reference implementations, prototypes, and reusable platform capabilities that accelerate delivery across the engineering organization.
• Own end-to-end technical delivery: Translate ambiguous customer support scenarios into clear technical requirements and take solutions from architecture and experimentation through implementation, deployment, operation, and continuous improvement.
• Define technical architecture and engineering standards: Establish architecture patterns, API and data contracts, code quality expectations, evaluation practices, deployment strategies, and observability standards for AI services. Drive technical consistency while allowing teams to evolve their solutions.
• Advance evaluation-driven AI development: Design and implement evaluation frameworks using rubrics, golden datasets, simulation, judge models, and offline and online evaluation. Define quality thresholds and graduation criteria for moving agents from experimentation through shadow mode and into autonomous operation.
• Own production quality and reliability: Ensure AI services meet demanding standards for correctness, availability, latency, scalability, cost, and safety. Diagnose complex production issues, lead technical incident response and root cause analysis, and engineer systemic improvements.
• Design for responsible and secure AI: Embed responsible AI principles, privacy protections, prompt-injection defenses, tool authorization, data boundaries, action controls, auditability, and fail-safe behavior into system architecture and implementation.
• Make consequential technical decisions: Evaluate models, frameworks, platforms, and architectural approaches using evidence from experiments and production telemetry. Balance solution quality with reliability, maintainability, latency, cost, and operational complexity.
• Influence product and platform roadmaps: Partner with product managers, researchers, platform engineers, and Azure service teams to shape technical strategy and platform capabilities. Translate customer support patterns and operational signals into durable engineering investments.
• Drive measurable customer and business impact: Instrument systems and use data to improve automation accuracy, case volume reduction, resolution time, engineering efficiency, and customer satisfaction. Connect architectural investments to measurable outcomes.
• Provide technical leadership across teams: Lead architecture reviews, resolve complex technical disagreements, and align engineers and partner organizations around shared designs. Communicate clearly with engineering teams, leaders, and non-technical stakeholders.
• Raise the engineering capability of the organization: Mentor engineers in system design, software engineering, applied AI, and evaluation-driven development. Create reusable guidance and help teams develop the judgment to determine where AI agents should and should not act autonomously.
Qualifications
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
Skills Required
- Bachelor's degree in Computer Science, Engineering, or a related technical field.
- 12+ years of professional software engineering experience designing, building, and operating production software systems.
- Proficiency in C# and/or Python, with experience designing and implementing production-quality services and distributed systems.
- Experience serving as a technical architect or technical lead for complex, cross-team engineering initiatives.
- Experience with applied AI systems, including LLM applications, prompt engineering, RAG architectures, agent frameworks, or machine learning services.
- Experience with cloud platforms, preferably Azure, and cloud-native practices including microservices, APIs, containers, CI/CD, infrastructure as code, and observability.
- Experience owning the full engineering lifecycle from problem definition and architecture through implementation, deployment, and production operations.
- Proficiency in software architecture, distributed systems, data structures, algorithms, API design, security, testing, and service reliability.
- Ability to influence technical direction and align multiple teams without direct management authority.
- Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- Experience architecting and shipping multi-agent orchestration systems or agentic AI services in production.
- Experience with AI evaluation systems, including rubrics, golden datasets, judge models, simulation, adversarial testing, and offline and online evaluation.
- Experience with Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, or similar enterprise AI platforms.
- Deep knowledge of RAG design, including indexing, retrieval, ranking, grounding, citation, knowledge freshness, and access control.
- Experience implementing agent security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
- Experience building highly available distributed services with rigorous performance, scalability, resiliency, observability, and cost-efficiency standards.
- Experience leading architecture and co-development efforts involving multiple product, platform, research, and partner engineering teams.
- Ability to move between strategic architecture and detailed implementation, including debugging complex interactions across models, prompts, tools, data, and distributed services.
- Business acumen and experience connecting technical investments to measurable customer outcomes.
- Track record of mentoring engineers and raising engineering standards without formal people-management responsibilities.
- Background in customer support engineering, supportability, diagnostics, or customer experience platforms.
Microsoft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.
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Fair & Transparent Compensation — Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
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Retirement Support — Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
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Parental & Family Support — Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.
Microsoft Insights
What We Do
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