Principal Software Engineer

Responsibilities

• AI-Native Architecture & Technical Strategy
• Architect the core intelligence layer of the platform, spanning data ingestion, embeddings, retrieval, graph reasoning, agents, and real-time inference.
• Define how LLMs and predictive models integrate across backend services, APIs, and user-facing experiences.
• Identify high-impact opportunities where generative, predictive, or autonomous AI can eliminate operational toil, improve system understanding, or enhance decision-making.
• Lead architectural decisions around model selection, evaluation, fine-tuning, and inference infrastructure (custom vs OSS vs managed APIs).
• Establish best practices for AI-first engineering, including prompt and schema design, context assembly, evaluators, guardrails, observability, and continuous model monitoring.
• Partner with product and leadership to align AI capabilities with customer outcomes, trust requirements, and long-term platform strategy.
• Full-Stack Applied AI Development
• Build end-to-end AI-powered features - from backend reasoning services to APIs and user-facing workflows.
• Design and implement production-grade LLM and agent workflows, including automated enrichment, anomaly explanation, topology discovery, change impact analysis, and natural language querying.
• Develop scalable backend systems for high-throughput inference, embedding generation, vector search, and graph traversal.
• Collaborate on or directly contribute to frontend experiences that make AI outputs understandable, actionable, and debuggable for users (e.g., explanations, confidence signals, provenance, and feedback loops).
• Implement retrieval-augmented generation (RAG) pipelines and hybrid search systems that combine structured data, graphs, and unstructured context.
• Write clean, well-structured, production-quality code—and champion AI-assisted development tools (Claude, Cursor, Windsurf, etc.) to improve velocity and correctness.
• Continuously evaluate emerging AI frameworks, agent runtimes, orchestration tools, and model APIs, integrating them where they drive real user value.
• Data, Infrastructure & Platform Foundations
• Design data models and pipelines that support learning, reasoning, and traceability across the platform.
• Build and evolve distributed systems that are observable, fault-tolerant, and cost-efficient under AI workloads.
• Partner with infrastructure and DevOps teams to shape deployment, scaling, monitoring, and rollback strategies for AI-driven services.
• Ensure AI systems meet enterprise requirements for reliability, security, explainability, and compliance.

Qualifications

• 10+ years of professional software engineering experience, including technical leadership in complex, high-scale systems.
• Proven experience architecting and shipping distributed systems with meaningful AI, automation, or intelligent decisioning components.
• Hands-on experience with LLMs, embeddings, vector databases, RAG pipelines, agent frameworks, or model integration patterns.
• Strong system design skills across APIs, data modeling, event-driven architectures, caching, storage, and performance optimization.
• Comfort working across the stack, including backend services and collaboration on user-facing or API-layer design.
• Proficiency in at least one modern programming language (Go, Rust, Python, Java, or C++).
• Experience mentoring senior engineers and driving engineering best practices.
• Familiarity with AI-assisted development workflows and modern DevOps/tooling.

Nice to Have

• Experience operationalizing ML or LLM workloads in production at scale.
• Background in microservices, event-driven systems, or real-time data pipelines.
• Exposure to frontend frameworks or strong product intuition around AI UX.
• Experience with high-throughput, low-latency, or mission-critical systems.
• Open-source contributions or demonstrated technical leadership in distributed systems or AI tooling.