Principal Software Engineer - Agentic PDLC/SDLC

Fetch is an AI & Data company that focuses on understanding business and building intelligent products. They are seeking a Principal Software Engineer to design and evolve intelligent platforms that enhance their Product and Software Development Life Cycles while leading the technical vision for automating engineering workflows.

Responsibilities:

• Architect Enterprise DevX Infrastructure
• Design and evolve scalable systems supporting AI-assisted development (e.g., GitHub Copilot, Claude Code integrations), autonomous code refactoring, simulation testing environments, automated OKR tracking, and feedback-driven observability
• Set Organizational Standards
• Redefine and enforce company-wide technical standards for system reliability, performance, observability, and architectural health across all PDLC/SDLC platforms
• Architect for Extreme Scale
• Lead the system design and evolution of distributed cloud-native developer platforms supporting autonomous code refactoring, simulation testing environments, and feedback-driven observability
• Establish Rigid AI-Validation Frameworks
• Define company-wide standards for automated contract, regression, and safety checks to ensure AI-generated or refactored code is rigorously validated before reaching production
• Simplify Systemic Complexity
• Proactively identify organization-level bottlenecks and redesign systems to minimize operational costs while maximizing long-term velocity and reliability
• Define Multi-Year Technical Vision
• Establish the direction for agentic development beyond a single organization, guiding multiple engineering areas and influencing Fetch's long-term success
• Solve the Unsolvable
• Resolve deeply complex, ambiguous, cross-organizational technical challenges that have blocked major company initiatives or that few others can solve
• Quantify Technical Tradeoffs
• Align technical strategy with company business direction, translating platform optimizations and AI capabilities into measurable numbers (e.g., cycle time reduction, defect escape drops, and review rework percentage decreases)
• Exercise "Do Not Build" Judgment
• Evaluate industry trends and external technologies critically, demonstrating high-signal judgment on when to leverage tools and where to block implementations to protect compliance, IP, or reliability
• Mentor at Scale
• Mentor and sponsor industry-level staff/principal engineers and managers across organizational boundaries, visibly growing their technical depth, systems thinking, and leadership capability
• Sustain Organizational Culture
• Build and foster inclusive technical communities internally and externally, maintaining morale, psychological safety, and alignment during periods of organizational change
• Navigate High-Stakes Disagreements
• Resolve complex, divergent cross-functional views (between Engineering, Product, Data Science, and Legal) with empathy, clarity, and data-driven principles

Requirements:

• 15+ years of software engineering experience, including a strong track record as a Principal Engineer or equivalent company-level technical authority, leading the architecture of distributed systems and large-scale enterprise developer platforms
• Deep experience designing, scaling, and evolving development platforms powered by agentic AI tools that support product development and the full software development lifecycle across large organizations
• Deep technical mastery in Developer Experience (DevX) or Platform Engineering, with proven experience building or scaling modern CI/CD pipelines, containerized environments, event streaming, and advanced telemetry layers
• Proven experience operationalizing GenAI workflows at scale, including hands-on application of multi-modal LLM agent frameworks (e.g., LangChain, Semantic Kernel, Haystack) or Retrieval-Augmented Generation (RAG) systems inside an enterprise engineering ecosystem
• Demonstrated company-wide impact leading multi-quarter, multi-team technical initiatives that fundamentally transformed developer velocity, code quality, or resource efficiency
• Expert-level fluency in metric trees, with the ability to tie complex platform architectures directly to north-star business KPIs while operating within strict, non-negotiable operational guardrails
• Strong data-driven intuition for risk modeling and tradeoff analysis, with a clear framework for balancing acceleration against blast-radius control and understanding where AI creates leverage versus where traditional optimization approaches are more effective
• Exceptional communication skills, with a proven ability to drive company-level technical decisions and communicate effectively across engineering, executive leadership, cross-functional stakeholders, and non-technical partners
• Industry eminence demonstrated through open-source contributions, technical publications, conference speaking, or other work that has shaped engineering practices across the broader technology community
• Proven success building first-party platform capabilities and automated frameworks that eliminate reliance on costly third-party development infrastructure and tooling
• Strong expertise operating within compliance and privacy constraints, including designing systems aligned with frameworks such as SOC2, GDPR, and CCPA while enforcing strict privacy and redaction boundaries across automated AI pipelines
• Expert-level proficiency in modern concurrent backend programming languages such as Go, Java, Kotlin, or Python, backed by a strong polyglot engineering background