Principal Software Engineer, AI
United StatesFull-TimePrincipal
Salary1 - 100,000 USD per year
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Job Details
- Experience
- 12+ years
- Required Skills
- PythonJava
Requirements
- 12+ years of software engineering experience, with strong focus on data platforms, search, or retrieval systems.
- Proven experience building production-grade knowledge graphs, search systems, or large-scale retrieval architectures.
- Deep expertise in RAG systems, vector databases, embeddings, and relevance tuning techniques.
- Strong understanding of multi-tenant access control, RBAC design, and secure data architecture tradeoffs.
- Experience designing developer platforms, APIs, and SDKs with strong adoption and usability outcomes.
- Solid background in data governance, lineage, security, and auditability in enterprise environments.
- Proven ability to lead cross-team technical strategy and deliver complex, distributed platform systems.
- Proficiency in Python and/or Java, with experience across backend systems and data infrastructure.
- Excellent communication and collaboration skills across engineering, product, and leadership stakeholders.
Responsibilities
- Lead the design and evolution of an AI Context Layer that powers retrieval, knowledge, and reasoning capabilities for enterprise AI systems.
- Collaborate with feature teams to understand context requirements and translate them into scalable platform capabilities and data products.
- Architect and build core systems including knowledge graphs, retrieval pipelines, RAG infrastructure, and search services.
- Define and implement access control, RBAC models, and governance frameworks for secure, multi-tenant AI data usage.
- Design and maintain developer-facing APIs, SDKs, and interfaces to ensure usability, scalability, and adoption across engineering teams.
- Partner with data engineering to ensure reliable, high-quality data pipelines and infrastructure alignment.
- Drive technical standards through architecture reviews, design discussions, and code reviews across multiple teams.
- Ensure data governance, lineage, and auditability requirements are met across all AI context systems.
- Contribute to hiring, onboarding, and internal enablement to scale platform adoption and team capability.
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