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Senior Applied AI Engineer

Microsoft


Job Location:

Hyderabad - India

Monthly Salary: Not provided by the employer
Posted: 24 October 2026 (Yesterday)
Application Deadline: 21 January 2027
Vacancies: 1 Vacancy

Department:

Software Engineering

Job Summary

Overview
Are you a customer-obsessed AI-curious problem-solver who thrives in an inclusive collaborative global team Join Engineering Operations (EngOps) the organization driving operational excellence across the Microsoft Cloud to strengthen quality reliability security and customer trust.
Our mission is to prevent issues before they happen by turning operational signals into action through AI-powered automation enabling measurable customer impact at cloud scale. Our culture of empowerment inclusion and growth mindset defines how we work.
Within EngOps ACES the Agent Fleet Operations (AFO) team is looking for an AI-first software engineer to join the Cloud Operational Demand Engineering (CØDE) organization and help advance our mission of driving operational demand to zero. Were seeking a hands-on engineer with a DevOps background CI/CD and release engineering safe deployment observability and always-on production operations who pairs that discipline with AI engineering skills and a learn-it-all mindset.
You are passionate about applying AI to solve real customer problems and energized by the challenge of eliminating operational toil.
In this role you will build and ship production AI solutions that reduce operational demand and improve customer outcomes. You will contribute to the technical execution that turns unresolved cases into autonomously resolved customer-delighting outcomes and help build the release observability and reliability practices that let a fleet of agents run safely in the customer path.
Role summary
As an individual contributor you will be hands-on building and improving the agentic patterns that reduce operational demand and improve customer satisfaction. This is a DevOps and production-operations engineering role applied to autonomous AI: you own the delivery quality and production health of the components you build not just their design. This role suits an engineer who has built and shipped production-scale AI systems and ideally the CI/CD safe-deployment and reliability practices around them and who wants to go deep on agentic resolution while collaborating closely across the team.
Team charter
Build operationalize and scale AI-native support experiences for Broad Commercial customers by transforming supportability knowledge into reusable versioned testable and measurable assets that power product experiences support engineers and autonomous agents. The team reduces support demand engineering toil and time-to-resolution through AI-driven resolution capabilities that continuously learn from customer signals unresolved cases and operational insights.
AFO operates a fleet of autonomous agents that resolve customer support cases with human oversight. This is a production DevOps discipline not a set of prototypes: agents run continuously in the customer path and released observed and improved with the same rigor as any always-on service. You and your team will build and run this system across several planes of a single AIOps suite:
Fleet execution and live safety: operating the agents that execute individual cases end to end evidence acquisition diagnosis reasoning response composition and safe escalation alongside the live-safety services that continuously watch sentiment risk and unsafe or irreversible actions and can pause autonomy or transfer a case to a human.
Evaluation and repair: running the evaluation systems that judge per-turn and completed-case quality and the repair pipelines that turn failures into durable fixes documentation diagnostics prompt/routing/policy changes reusable skills or product work items routed to the right engineering team.
A control and learning plane: owning the lifecycle state machine that moves every change from observed detected diagnosed repair candidate independently evaluated approved shadowed canaried released production-verified with explicit expand pause revise


Responsibilities
  • Build agentic resolution capabilities for AFO
  • Build and improve agentic patterns that raise autonomous resolution rate accuracy and customer satisfaction contributing to the teams technical execution.
  • Deliver engineering for Agentic AI solutions
  • Deliver end-to-end features across autonomous AI systems including orchestration reasoning retrieval action execution and human escalation workflows. Take capabilities from prototype through production across support surfaces while partnering effectively across dependent engineering teams.
  • Contribute to technical direction and engineering standards
  • Help shape the technical foundation for agent development by implementing architectures design patterns evaluation frameworks and reusable platform capabilities.
  • Apply and help improve standards for prompts tool interfaces testing versioning code quality CI/CD and safe deployment practices (shadow canary progressive rollout automated rollback) so agent behavior remains reliable observable and scalable.
  • Build evals that improve customer confidence that our agents are trustworthy in understanding customer sentiment and intent and in validating agentic actions.
  • Contribute to production health and reliability
  • Help run the service with a DevOps/SRE operating model. Support availability latency cost per resolution SLOs and error budgets regression detection incident response and the on-call rotation for a fleet of agents acting autonomously in the customer path. Build the observability needed to explain why an agent did what it did and drive durable fixes rather than repeated mitigations.
  • Build and maintain evaluation systems
  • Contribute to the evaluation framework that determines whether agents are safe and effective in production. Build and maintain golden datasets rubrics judge agents frozen anchors and offline/online evaluation pipelines. Use evaluation signals to gate releases and continuously improve quality through production feedback loops.
  • Contribute to measurable business impact
  • Help move key business outcomes including support demand reduction autonomous resolution rate resolution quality time-to-resolution engineer productivity and customer satisfaction. Ensure the capabilities you build are measurable and directed toward the highest-impact opportunities.
  • Turn Customer Signals into Engineering Insight
  • Build mechanisms that convert unresolved cases customer feedback and dissatisfaction signals into actionable engineering insights. Help ensure recurring failure patterns improve future agent capabilities and route product fixes back to the appropriate engineering teams.
  • Grow your craft and share learnings
  • Deepen your agentic AI and DevOps craft and extend the teams impact by sharing proven patterns tools and operational learnings with your peers.



Qualifications
Bachelors Degree in Computer Science Engineering or related field AND 8 years technical engineering experience in software engineering applied AI/ML or distributed systems OR equivalent experience.
8 years of IT experience and 3 years of experience using Agentic AI
Demonstrated hands-on experience building and shipping AI or machine learning systems in production.
Demonstrated DevOps or SRE background: hands-on experience with CI/CD and release engineering safe-deployment practices (e.g. blue/green canary progressive rollout automated rollback) observability and monitoring and participation in production incident response and on-call for always-on services.
Preferred qualifications
Masters degree in Computer Science AI or a related field.
Experience building and shipping multi-agent orchestration systems or agentic AI services in production.
Experience with AI evaluation frameworks: rubrics golden datasets judge agents offline and online evaluation.
Experience with Azure AI Foundry Azure OpenAI or comparable enterprise AI platforms.
Familiarity with agent security patterns: prompt injection defense tool access controls data boundary enforcement.
DevOps/SRE toolchain depth: CI/CD pipelines infrastructure-as-code deployment automation and progressive-delivery tooling (shadow canary feature/rollout gating automated rollback).
Observability engineering: building tracing metrics and logging for distributed or agentic systems and using them to drive durable fixes rather than repeated mitigations.
Reliability practice: working with SLIs/SLOs and error budgets and taking part in the operational rhythm (live-site reviews post-incident reviews repair-item and corrective-action tracking) that keeps a fleet healthy.
Experience contributing to cross-org or co-development engineering delivery with partner teams.
Business acumen with the ability to connect engineering work to measurable customer and business outcomes.
Background in customer support engineering supportability or customer experience platforms.
Experience operating always-on production services including on-call participation and incident response.

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.


Required Experience:

IC