Principal Software Engineer

Principal Software Engineer Role Overview

As a Principal Engineer within the Decision Stream program, you will combine enterprise‑scale technical leadership with hands‑on engineering for the next‑generation Decision Management Platform. This is not a strategy‑only role. You will actively design, code, prototype, and validate core platform capabilities, using modern AI‑assisted development tools as part of day‑to‑day software engineering to move faster, improve quality, and help teams adopt better ways of building.

Key areas of focus include leveraging disruptive technologies in real‑time AI inferencing and decisioning to improve product effectiveness, increase business delivery, strengthen technical resilience, and lower cost of ownership. You will work closely with technology executives, senior leaders, and engineers to shape the overall AI & DPE technology strategy.

• Build software, tooling, and platform capabilities.
• Design and implement large scale distributed systems.
• Develop reusable services, patterns, and integrations.
• Contribute to new product and prototype development from concept through validation.

• Evaluate systems, frameworks, and tools across quality, cost, latency, scalability, reliability, and maintainability.
• Apply sound engineering judgment to trade‑offs in distributed systems design and architecture.

• Apply AI tools as part of daily engineering practice to real product and platform problems.
• Teach and model the adoption of AI‑assisted development, modern languages, and current engineering practices.
• Improve developer experience through automation, AI‑assisted workflows, and platform thinking.
• Advocate learnings, prototypes, and best practices across the organization.

• Own and improve end‑to‑end customer experience across a portfolio of services and applications.
• Simplify and optimize architecture strategies to balance cost, performance, and business value.
• Apply judgment and experience to make trade‑offs between competing priorities and technical constraints.

• Lead architectural design for complex, enterprise‑wide initiatives spanning multiple services and programs.
• Drive organization‑wide initiatives to advance software engineering craftsmanship and best practices.
• Represent the organization through public speaking, technical blogs, and white papers on emerging technologies.
• Participate in principle‑level architecture reviews and resolve enterprise‑wide technical and regulatory challenges.

• Mentor engineers at all levels, fostering technical growth and leadership.
• Conduct technical interviews to raise the performance bar and attract top talent.
• Provide unbiased, accomplishment‑based recommendations for promotions.
• Champion AI‑assisted engineering as a default working practice and align it with organizational values.

• AI‑native engineer — uses AI‑assisted development as default.
• Innovation leader — builds systems at massive scale and availability.
• Streaming‑first mindset — experience with low‑latency pipelines.
• Proven outcomes — delivers impactful, production‑ready systems.
• Engineering culture champion — drives best practices and transparency.
• Collaborative — works across engineering and data science teams.

• Data architectures for decisioning: lake houses, delta lakes, distributed logs, and product‑aligned data models.
• Feature catalogs and engineering platforms for reusable, governed features that can be defined, discovered, validated, and served across batch and real‑time decisioning.
• Decisioning data models covering events, derived features, reference data, labels, outcomes, and policy data consumed by rules, models, and agentic workflows.
• Data contracts, lineage, freshness, and quality controls that keep decisioning data trustworthy, explainable, and production ready.

• Event streaming and high‑throughput data pipelines.
• Low‑latency data technologies – distributed caches, in‑memory data…