Principal Applied AI Engineer
Microsoft · Posted Oct 19
Software, cloud computing, artificial intelligence, and hardware services
India, Telangana, HyderabadFull-timeOnsiteLead/Staff12+ years₹70.0L–₹1.4Cr yearly41 applicants
SoftwareCloud ComputingArtificial IntelligenceConsumer ElectronicsInformation TechnologyPublic Company
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About the role
The Engineering Operations (EngOps) team works to improve Microsoft Cloud quality, security, and reliability through deep engineering engagements with customers and internal teams. Its ACES CODE engineering team builds domain-specific agents and services that move Azure customer support from reactive, human-only workflows to AI-first systems resolving issues at Azure scale. This Principal Applied AI Engineer role is a hands-on technical architect and developer position focused on LLM orchestration, multi-agent coordination, RAG pipelines, tool integration, and evaluation systems for production agentic AI services.
What you will do
- 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.
Skills used in this role
C#PythonAzureLLM OrchestrationRAGMulti-Agent SystemsPrompt EngineeringMachine LearningAgent FrameworksMicroservicesAPIsContainersCI/CDInfrastructure as CodeObservabilityDistributed SystemsAzure AI FoundryAzure OpenAIMicrosoft Agent FrameworkSoftware ArchitectureSecurityTesting
What the employer is looking for
- 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 demonstrated ability to design and implement 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.
- Demonstrated success owning the full engineering lifecycle, from problem definition and architecture through implementation, deployment, and production operations.
- Profilency in software architecture, distributed systems, data structures, algorithms, API design, security, testing, and service reliability.
- Proven ability to influence technical direction and align multiple teams without direct management authority.
Preferred qualifications
- 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 standards for performance, scalability, resiliency, observability, and cost efficiency.
- Experience leading architecture and co-development efforts involving multiple product, platform, research, and partner engineering teams.
- Demonstrated ability to move between strategic architecture and detailed implementat...
Benefits and support
- Compensation and benefits are detailed in the job posting
About Microsoft
Microsoft Corporation is a multinational technology company that develops, licenses, and supports a wide range of software products, services, and hardware devices. Best known for its Windows operating systems, Microsoft 365 productivity suite, and Xbox gaming consoles, the company has expanded heavily into enterprise cloud computing via Azure and advanced artificial intelligence solutions. As one of the world's most valuable public enterprises, it empowers individuals and businesses globally through innovative digital transformation.
- Industry
- Software
- Company size
- 228,000+ employees
- Founded
- April 4, 1975
- Location
- Redmond, Washington, USA
- Funding stage
- Public Company
Leadership
SN
Satya Nadella
Chairman and Chief Executive Officer
AH
Amy Hood
Chief Financial Officer
BS
Brad Smith
Vice Chair and President
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