3 days ago
Hyderābād, IndiaStaff+
Responsibilities
- Architect and build production agentic AI systems, including LLM orchestration, multi-agent workflows, RAG pipelines, tool integrations, memory, state management, and human-in-the-loop experiences.
- Write, review, and maintain production-quality C# and Python code, including critical components, reference implementations, prototypes, and reusable platform capabilities.
- Own technical delivery from customer-support problem definition and architecture through experimentation, implementation, deployment, production operation, and continuous improvement.
- Define architecture patterns, API and data contracts, code-quality expectations, evaluation practices, deployment strategies, and observability standards for AI services.
- Build evaluation frameworks using rubrics, golden datasets, simulation, judge models, and offline and online evaluation, with quality thresholds for autonomous operation.
- Ensure production quality and reliability across correctness, availability, latency, scalability, cost, and safety; diagnose incidents and lead root-cause analysis.
- Embed responsible and secure AI practices, including privacy protections, prompt-injection defenses, tool authorization, data boundaries, action controls, auditability, and fail-safe behavior.
- Evaluate models, frameworks, platforms, and architectural approaches using experiments and production telemetry.
- Partner with product managers, researchers, platform engineers, and Azure service teams to shape technical strategy and platform capabilities.
- Lead architecture reviews, resolve technical disagreements, align partner organizations, and mentor engineers across teams without formal people management authority.
Requirements
- 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-based applications, prompt engineering, RAG architectures, agent frameworks, or machine learning services.
- Experience with cloud platforms, preferably Azure, and cloud-native engineering 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.
- A master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field is preferred.
- Preferred experience includes Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, multi-agent orchestration, AI evaluation systems, deep RAG design, agent security patterns, and highly available distributed services.
- Preferred qualifications include cross-team architecture leadership, complex debugging across models and distributed services, business acumen, mentoring experience, and customer support engineering or diagnostics experience.
Categories
About Microsoft
Microsoft develops operating systems, productivity software, cloud services, developer tools, and consumer devices for individuals, enterprises, and governments. Its main products include Windows, Microsoft 365, Azure, Visual Studio/GitHub, Xbox, and LinkedIn; revenue comes from software subscriptions and licenses, cloud consumption, hardware sales, and advertising. Founded in 1975 and headquartered in Redmond, Washington, Microsoft is a public company traded on Nasdaq.