Principal Software Engineer

Own Execution Excellence and Engineering Throughput 

• Translate the team leader’s vision and strategy into clear engineering priorities, executable plans, milestones, decision points, and accountable delivery rhythms. 

• Instrument the end-to-end engineering system to identify constraints in planning, design, implementation, review, evaluation, deployment, and operations; use evidence to improve flow rather than optimizing isolated activities. 

• Own measurable improvements in cycle time, deployment frequency, work-in-progress, quality, reliability, and engineer time spent on differentiated work, while avoiding output metrics that reward activity over customer value. 

Transform Engineering with AI-Native Practices 

• Redesign the software-development lifecycle around AI-assisted and agentic workflows for discovery, design, coding, testing, evaluation, security review, documentation, incident response, and service operations. 

• Build reusable agents, context systems, evaluation harnesses, and paved paths that allow engineers to move from intent to validated production changes with less friction and stronger safeguards. 

• Establish standards for human oversight, provenance, secure tool use, data boundaries, review depth, and verification so increased velocity does not compromise trust, maintainability, or engineering judgment. 

Lead Hands-On Delivery and Continuous Improvement 

• Work alongside engineers on the highest-leverage problems—prototype AI-native workflows, review critical designs and changes, diagnose systemic failures, and remove blockers that impede delivery. 

• Create reusable frameworks, templates, automation, and engineering standards that reduce cognitive load and enable teams to deliver faster without compromising reliability, security, or responsible AI. 

• Run disciplined learning loops through delivery reviews, retrospectives, experiments, and decision records; scale proven practices and retire processes or tools that do not improve outcomes. 

Convert Priorities into Customer Outcomes 

• Partner with product, customer, and field teams to break strategic priorities into thin, testable increments that produce early evidence and shorten time to customer value. 

• Coordinate dependencies and resolve execution tradeoffs across Security, Azure, AI, Data, Research, and Customer Experience while keeping teams aligned to the established vision and strategy. 

• Connect delivery measures to customer adoption, task success, security posture, quality, reliability, time-to-value, and responsible-AI performance. 

Own Production Trust and Responsible AI 

• Establish rigorous evaluation and release criteria for model quality, groundedness, safety, fairness, privacy, security, and abuse resistance. 

• Build telemetry and feedback loops that connect system behavior to customer outcomes, enabling rapid detection, learning, and continuous improvement. 

• Lead technical response to high-severity issues and ensure learnings become systemic improvements in architecture, testing, governance, and operations. 

What Success Looks Like 

• The team reliably converts vision and strategy into prioritized, executable work with clear ownership, rapid decisions, and predictable delivery. 

• AI-native engineering practices materially reduce cycle time and toil while increasing deployment frequency, evaluation coverage, quality, and production confidence. 

• Engineers spend more time on differentiated customer problems because repetitive work, context gathering, verification, documentation, and operational tasks are safely augmented or automated. 

• Reusable agents, context store for agents, paved paths, and learning loops spread across the organization, compounding throughput gains without weakening security, responsible AI, or human accountability. 

Required Qualifications: 

Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications 

• Bachelor’s degree in Computer Science, Engineering, Data Science, a related technical field, or equivalent practical experience, plus substantial experience delivering complex production software and AI systems. 

• Demonstrated success improving execution across engineering teams through operating mechanisms, platform leverage, automation, and measurable constraint removal. 

• Deep hands-on expertise with modern AI engineering, including LLM application patterns, agent orchestration, retrieval, evaluation, data pipelines, and production operations. 

• Experience applying AI-assisted or agentic practices across the software-development lifecycle and establishing safeguards that make those practices dependable at scale. 

• Strong software-engineering skills in languages such as Python, C#, Java, or C++, with a record of shipping reliable cloud services and developer-facing platforms. 

• Proven ability to lead through influence, resolve cross-team execution tradeoffs, and communicate clearly with engineers, leaders, customers, and partners. 

• Experience embedding security, privacy, reliability, and responsible-AI principles into architecture, delivery, and engineering practices. 

• Experience architecting AI systems on Azure or another hyperscale cloud, including identity, data, compute, networking, observability, and deployment infrastructure. 

• Experience building internal developer platforms, coding agents, evaluation systems, or workflow automation that produced measurable improvements in engineering throughput. 

• Knowledge of flow and delivery measures such as cycle time, deployment frequency, work-in-progress, change-failure rate, recovery time, and developer experience. 

• Experience establishing evaluation programs for nondeterministic systems, including offline benchmarks, red teaming, online experimentation, and human-feedback mechanisms. 

• Track record of creating reusable platforms or standards that materially improved engineering velocity, product quality, or customer outcomes across an organization.