Software Engineer II – Finance Data & Experiences (FD&E) AI
Job Description
Software Engineer II – Finance Data & Experiences (FD&E) AI Platform TeamReady to shape the future of how Microsoft runs its $250B+ finance operations with AI? We are looking for an energetic, curious engineer who is passionate about Artificial Intelligence and data to help drive the next generation of intelligent finance systems. The Finance Data & Experiences (FD&E) Platform team is on a mission to redefine how Microsoft measures, monitors, and optimizes its global business by harnessing cutting-edge AI. This is a unique opportunity to innovate boldly and apply the latest in large-scale AI (think LLMs, AI agents, and next-gen analytics) to high-impact finance scenarios. You will thrive in a fast-paced, cross-functional environment, bring fresh thinking to complex problems, and take ownership of end-to-end outcomes. Join us and drive AI-powered transformation in Microsoft Finance, pushing boundaries and delivering new capabilities that make our business more data-driven, efficient, and intelligent. 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. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day. This role follows a hybrid work model, requiring on-site presence at our Redmond campus three days a week.
Locations
- Redmond, Washington, United States, Redmond, Washington, United States
Salary
Salary not disclosed
Required Qualifications
- Bachelor’s degree in computer science or related technical field AND 2+ years technical engineering experience with coding in one or more languages including, but not limited to SQL, SSAS, Python, Spark, Scale, C#, C++, and JavaScript OR equivalent experience. (degree)
- 1+ years building AI features or applications using machine learning or large language models. Examples include chat assistants, agents, retrieval augmented generation, classification or summarization, or prompt and tool-based workflows. (degree)
- 2+ years building cloud services on Azure, Amazon Web Services, Google Cloud Platform, or an equivalent environment. (degree)
- 4+ years of software development experience with large scale data processing systems or online services. (degree)
- Experience with Azure services or another major cloud. Examples include Azure Functions, Kubernetes, Azure Machine Learning, Azure OpenAI Service, Azure AI Studio, Azure Data Explorer, Spark or Databricks, and Microsoft Fabric OneLake. (degree)
- Familiarity with AI and machine learning concepts, embeddings and vector search, prompt design and prompt evaluation, and metrics for groundedness, quality, safety, and latency. (degree)
- Experience taking research style prototypes to production, including experiment design, ablation studies, and result reporting. (degree)
- Understanding of version control with Git and continuous integration and continuous delivery practices. (degree)
- Strong analytical skills and a structured approach to system and service design. (degree)
- Ability to communicate technical details clearly and collaborate across teams. (degree)
- Finance domain knowledge is a plus. (degree)
- Experience with Agile and iterative development practices. (degree)
Preferred Qualifications
- 4+ years of software development experience with large scale data processing systems or online services. (degree)
- Experience with Azure services or another major cloud. Examples include Azure Functions, Kubernetes, Azure Machine Learning, Azure OpenAI Service, Azure AI Studio, Azure Data Explorer, Spark or Databricks, and Microsoft Fabric OneLake. (degree)
- Familiarity with AI and machine learning concepts, embeddings and vector search, prompt design and prompt evaluation, and metrics for groundedness, quality, safety, and latency. (degree)
- Experience taking research style prototypes to production, including experiment design, ablation studies, and result reporting. (degree)
- Understanding of version control with Git and continuous integration and continuous delivery practices. (degree)
- Strong analytical skills and a structured approach to system and service design. (degree)
- Ability to communicate technical details clearly and collaborate across teams. (degree)
- Finance domain knowledge is a plus. (degree)
- Experience with Agile and iterative development practices. (degree)
Responsibilities
- Design, implement, test, and operate shared AI capabilities, including prompt orchestration and versioning, retrieval augmented generation (RAG) adapters, secure tool and skill catalogs, model routing, Responsible AI guardrails, evaluation, and telemetry.
- Build copilots, agents, and automation for scenarios such as financial planning and analysis, close and consolidation, revenue and margin analysis, spend governance, and compliance and controls. Integrate with Microsoft Fabric OneLake, Azure Data Explorer, SQL, Spark, semantic models, and vector search.
- Prototype and evaluate emerging capabilities such as tool use, function calling, long context strategies, structured reasoning, and multi agent patterns. Run small experiments, document findings, and graduate successful approaches into platform features and production experiences.
- Use Azure OpenAI Service and Azure AI Services to integrate models. Build offline evaluation pipelines and golden sets. Measure groundedness, accuracy, safety, latency, and cost. Run experiments to compare prompts, tools, and model variants.
- Write unit, integration, load, and prompt flow tests. Implement monitoring, logging, alerting, and tracing. Participate in on call, incident response, and post incident reviews. Help meet service level objectives and service level indicators.
- Apply secrets management, role-based access, encryption in transit and at rest, and records retention. Contribute to risk assessment, red teaming, and content filtering to ensure safe and compliant AI.
- Work with product managers and partners to clarify requirements. Engineering leads author user stories and technical designs. Break down features, estimate work, and communicate progress, risks, and tradeoffs clearly.
- Leverage GitHub Copilot for boilerplate code, tests, data transformations, and quick prototypes. Share effective prompts and patterns to improve team velocity and learning.
Travel Requirements
3 days / week in-office
