Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.We are seeking a
AI/ML Engineer to
lead the design, development, and deployment of agentic AI systems and multi-agent architectures
. You will focus on
large language models (LLMs), intelligent agents, and Azure-native deployment
to automate complex incentive workflows. Collaborating closely with our
Data Engineering team
(handling pipelines, integrations, and reporting), you will own the
AI core functionalities
—from document parsing to agent orchestration, scaling solutions iteratively to handle increasing volumes and complexities.
Responsibilities
- Lead the design, development, and deployment of agentic AI and multi-agent systems to automate incentive workflows (ingestion → parsing → calculation → reporting).
- Apply NLP techniques to parse T&C documents, extracting eligibility rules, identifiers, and business logic into structured outputs (e.g., JSON).
- Implement prompt engineering, fine-tuning, and inference for LLMs (Azure OpenAI, Hugging Face) to handle business logic, corrections, and decision-making.
- Architect and modernize cloud-native applications using APIs, microservices, event-driven designs, and containerized deployments (AKS, Functions, Logic Apps).
- Build and manage MLOps workflows (CI/CD pipelines, retraining, monitoring, governance) to ensure robust AI lifecycle management.
- Deploy production-grade AI systems at scale with self-correction, monitoring, and failover mechanisms.
- Implement Retrieval-Augmented Generation (RAG) and knowledge-driven AI pipelines for enhanced reasoning and decision-making.
- Collaborate with Data Engineers to integrate AI agents with Azure Synapse, SQL, Dataverse, and Power BI for dynamic incentive computation and reporting.
- Iteratively scale the system: from handling a few T&C use cases to supporting dozens of documents, multiple data sources, and high-volume incentive calculations.
- Stay up to date on agent orchestration, reinforcement learning, and semantic search to enhance agent autonomy and system efficiency.
Skills to have
- Professional experience in AI/ML engineering, with a strong focus on LLMs and intelligent agent systems.
- Hands-on expertise with agentic AI frameworks (LangChain, AutoGen, CrewAI, MetaGPT, LangGraph).
- Proven experience in LLM orchestration and NLP pipelines (Hugging Face, OpenAI APIs, Semantic Kernel, PromptFlow).
- Strong coding skills in Python (primary), with experience in Java, .NET, TypeScript, C#, or C++.
- Proficiency in Azure ecosystem: Azure OpenAI, Cognitive Services, Azure ML, Synapse, Data Factory, Microsoft Fabric.
- Experience in cloud-native deployments (AKS, Functions, Logic Apps, Event Hubs, Key Vault, Application Insights).
- Knowledge of distributed computing (Ray, Dask), messaging systems (Kafka, Redis), and AI Ops/Security (Gen AI Ops, Sentinel, monitoring).
Qualifications
Required Qualifications
- Bachelor’s or master’s degree in computer science, AI, Machine Learning, or a related field.
- Hands-on experience with Azure OpenAI or similar LLM platforms (e.g., OpenAI API, Hugging Face).
- Expertise in LLMs, including LangChain for agent building and prompt engineering techniques.
- Proficiency in Python for developing AI solutions, with strong skills in libraries like LangChain, NLTK, or spaCy for NLP tasks.
- Proven experience building autonomous AI agents or multi-agent systems for real-world applications, such as business process automation.
- Familiarity with NLP for parsing unstructured documents (e.g., T&C extraction). Understanding of cloud infrastructure setup, particularly Azure services (e.g., Functions, Key Vault, Application Insights) for deploying and scaling AI workloads. Strong problem-solving skills and ability to work in an iterative, agile environment.
Preferred Qualifications
- Experience scaling AI systems to handle increased workloads, such as more incentive calculations, using serverless architectures or containerization.
- Knowledge of business domains like incentive management or contract analysis.
- Familiarity with tools like CrewAI for agent orchestration or Azure ML for model fine-tuning.
- Experience collaborating with Data Engineers on integrations involving cubes, SQL servers, or Power BI.
Microsoft is an equal opportunity employer. Consistent with applicable law, 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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
