Principal AI Engineer in United States at Jobgether
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Job Description
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Principal AI Engineer in United States.
In this role, you will define and build the core intelligence and inference layer of a complex, enterprise-grade AI platform powering compliance-critical workflows. You will architect the systems that enable agentic applications to operate over structured knowledge graphs, hybrid retrieval pipelines, and large-scale document intelligence. This is a deeply technical, high-ownership position where you will shape how reasoning, memory, and evaluation are implemented in production AI systems. You will work directly with senior leadership and cross-functional teams spanning data engineering, product, and domain experts in regulated tax environments. The role requires designing systems that are not only performant and scalable, but also auditable, defensible, and aligned with expert human judgment. You will have broad architectural authority and will influence long-term platform direction in a fast-evolving AI landscape.
- Architect and own the AI/ML “intelligence layer” powering agentic workflows, including retrieval, reasoning, memory, and inference systems.
- Design and implement a domain knowledge graph capturing entities, relationships, and expert tax reasoning in a structured, versioned format.
- Build hybrid retrieval systems combining graph traversal, vector search, and structured queries with intelligent orchestration.
- Develop entity resolution, relationship extraction, and structured data pipelines across large-scale document sources and enterprise datasets.
- Define and implement systems for capturing expert judgment as first-class, versioned data for reuse and evaluation.
- Build evaluation frameworks to measure retrieval quality, reasoning accuracy, and alignment with expert ground truth.
- Design model serving infrastructure, including real-time inference endpoints, model lifecycle management, and experiment tracking.
- Integrate LLM systems with production-grade cost optimization, observability, and reliability patterns.
- Create developer-facing APIs and SDKs enabling product teams to build agentic workflows on top of platform primitives.
- Establish auditability, versioning, and reproducibility across models, prompts, graphs, and evaluation datasets.
- Collaborate closely with data engineering, product engineering, and domain experts to ensure system correctness and usability.
- 10+ years of software engineering experience, including deep expertise in distributed systems, data platforms, or search/retrieval systems.
- 5+ years of hands-on ML engineering or AI platform development experience in production environments.
- Proven experience building or significantly extending production knowledge graphs with schema design and entity resolution.
- Strong background in hybrid retrieval systems combining vector search, graph-based retrieval, and structured querying.
- Production experience with RAG systems, LLM integration, and retrieval optimization at scale.
- Experience building AI systems for document-intensive workflows such as extraction, classification, or structured reasoning.
- Hands-on experience with model serving infrastructure, including latency-sensitive and cost-sensitive production systems.
- Expertise in designing evaluation frameworks using real-world or expert-labeled ground truth data.
- Strong Python engineering skills; experience with TypeScript/Node.js is a plus.
- Experience designing developer-facing APIs, SDKs, or platform services used by other engineering teams.
- Familiarity with MCP or equivalent agent-tooling frameworks and production AI orchestration patterns.
- Strong system design skills with the ability to operate in ambiguous, early-stage environments.
- Excellent communication skills with the ability to work across technical and non-technical stakeholders.
- Preferred: experience in regulated domains such as finance, legal, tax, or compliance-heavy industries.
- Preferred: familiarity with ontologies, regulatory systems, or knowledge representation in structured domains.
- Competitive compensation and comprehensive benefits package (US-based).
- Fully remote or hybrid flexibility depending on location.
- Health, dental, vision, life, and disability insurance coverage.
- 401(k) retirement plan with employer participation.
- Generous vacation policy and paid parental leave.
- Additional perks including FSA/HSA options, dependent care savings, pet insurance, and EAP support.
- Strong learning and career development opportunities in a high-growth AI environment.
- Opportunity to work on cutting-edge AI systems with real-world regulatory and compliance impact.
- High autonomy and ownership in shaping core platform architecture.