Hire RAG Architects | TeamStation AI

Retrieval-Augmented Generation (RAG) is the key to building LLM applications that are grounded in your private data, reducing hallucinations and providing accurate, up-to-date responses. Our RAG Architects are experts in designing and building the complex data pipelines that power these systems. We provide talent vetted for their expertise in data chunking, embedding models, vector databases, and retrieval strategies.

Are your LLM's answers out-of-date or irrelevant?

The Problem

LLMs trained on public data have no knowledge of your internal documents, databases, or real-time information. This leads to generic, unhelpful, or completely incorrect answers, making the application useless for specific business contexts.

The TeamStation AI Solution

Our RAG Architects are experts in building data ingestion and retrieval pipelines. They are vetted on their ability to efficiently process, chunk, and embed your proprietary data into a vector database, and to design effective retrieval strategies that provide the LLM with the precise context it needs to generate relevant and accurate answers.

Proof: Measurable improvement in response accuracy and relevance.
Is your retrieval system returning low-quality or noisy context?

The Problem

Simply finding 'similar' documents is not enough. Poor chunking strategies, basic retrieval algorithms, and a lack of re-ranking can provide the LLM with irrelevant or conflicting information, leading to worse, not better, answers.

The TeamStation AI Solution

Our architects are masters of advanced retrieval techniques. They can implement sophisticated chunking strategies, use hybrid search (keyword + vector) to improve recall, and apply re-ranking models to ensure only the most relevant, high-quality context is passed to the LLM.

Proof: Advanced retrieval strategies for high-quality context.

How We Measure Seniority: From L1 to L4 Certified Expert

We don't just match keywords; we measure cognitive ability. Our Axiom Cortex™ engine evaluates every candidate against a 44-point psychometric and technical framework to precisely map their seniority and predict their success on your team. This data-driven approach allows for transparent, value-based pricing.

L1 Proficient

Guided Contributor

Contributes on component-level tasks within the RAG Architect domain. Foundational knowledge and learning agility are validated.

Evaluation Focus

Axiom Cortex™ validates core competencies via correctness, method clarity, and fluency scoring. We ensure they can reliably execute assigned tasks.

$20 /hour

$3,460/mo · $41,520/yr

± $5 USD

L2 Mid-Level

Independent Feature Owner

Independently ships features and services in the RAG Architect space, handling ambiguity with minimal supervision.

Evaluation Focus

We assess their mental model accuracy and problem-solving via composite scores and role-level normalization. They can own features end-to-end.

$30 / hour

$5,190/mo · $62,280/yr

± $5 USD

L3 Senior

Leads Complex Projects

Leads cross-component projects, raises standards, and provides mentorship within the RAG Architect discipline.

Evaluation Focus

Axiom Cortex™ measures their system design skills and architectural instinct specific to the RAG Architect domain via trait synthesis and semantic alignment scoring. They are force-multipliers.

$40 / hour

$6,920/mo · $83,040/yr

± $5 USD

L4 Expert

Org-Level Architect

Sets architecture and technical strategy for RAG Architect across teams, solving your most complex business problems.

Evaluation Focus

We validate their ability to make critical trade-offs related to the RAG Architect domain via utility-optimized decision gates and multi-objective analysis. They drive innovation at an organizational level.

$50 / hour

$8,650/mo · $103,800/yr

± $10 USD

Pricing estimates are calculated using the U.S. standard of 173 workable hours per month, which represents the realistic full-time workload after adjusting for federal holidays, paid time off (PTO), and sick leave.

Core Competencies We Validate for RAG Architect

Data Ingestion and Chunking Strategies
Embedding Model Selection and Optimization
Vector Database Architecture and Indexing
Advanced Retrieval Strategies (Hybrid Search, Re-ranking)
Evaluation of RAG Pipeline Performance

Our Technical Analysis for RAG Architect

Candidates are required to design an end-to-end RAG architecture for a complex knowledge base. They must justify their choice of chunking strategy, embedding model, and vector database index. We assess their ability to design a retrieval system that can handle complex queries and their plan for evaluating and improving the performance of the entire pipeline.

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