Hire an LLM Engineer, Raleigh

Hire an LLM engineer in Raleigh.

The Research Triangle, SAS Institute in Cary, Red Hat in Durham, NC State in Raleigh, UNC and Duke in the surrounding area, has specific LLM engineering requirements that differ from general commercial applications. Large scientific corpora need domain-adapted embeddings. Research paper tables require two-pass extraction, not naive prompting. Enterprise software at SAS and Red Hat requires prompt management infrastructure, not prompt strings stored in code.

Each of these is a solvable engineering problem with known solutions. SPECTER2 for academic paper retrieval. Structured table extraction before the LLM step. Git-based prompt registries with evaluation CI. Confidence scores on every extraction.

Fixed-price engagements, scoped after a technical assessment of your corpus and infrastructure.

How we work

Describe your Research Triangle LLM engineering problem: corpus type, document format, or production infrastructure gap.

Four technical problems the Research Triangle runs into.

General embeddings underperform on scientific corpora

For RAG over academic papers at NC State, UNC, or Duke, standard embeddings like text-embedding-3-large retrieve less relevant documents than SPECTER2, a model trained on scientific literature citation graphs. The gap is largest in specialized subfields where general training data coverage is thin.

Table extraction from research papers fails with naive prompting

Complex research tables (multi-level headers, merged cells, footnote-referenced units) require structure extraction before the LLM step. Two-pass extraction: pdfplumber for structure, then the LLM with structured table context as input.

Prompts treated as configuration strings, not code artifacts

At SAS and Red Hat scale, prompts stored in env variables or hardcoded strings have no review history, no rollback path, and no evaluation gate. They need to be in Git, tested in CI, and deployed via a registry.

Research extraction without confidence scores

Pipelines that write uncertain extractions silently corrupt research databases. Confidence scoring (model scores plus rule-based validation) identifies the ambiguous 10 to 20% of records that need human review.

What we build for Research Triangle clients.

SPECTER2 embedding pipeline

Domain-adapted embeddings for scientific paper corpora. Higher retrieval precision for RAG over biomedical, agricultural, and computer science literature.

Two-pass table extraction

pdfplumber for table structure, LLM for value extraction with structure as context. Handles multi-level headers, merged cells, and footnote-referenced units.

Git-based prompt registry

Prompts as versioned TypeScript or Python functions. CI evaluation on every prompt PR. Registry service for feature-to-version mapping and instant rollback.

Multi-layer confidence scoring

Model probability, rule-based validation (format, range, cross-field consistency), and historical baseline comparison. Records flagging on any layer route to review queue.

RAG pipeline with domain embeddings

Retrieval-augmented generation over large scientific corpora using SPECTER2 for indexing and retrieval, GPT-5 or Claude for generation with cited sources.

Evaluation CI for research pipelines

Labeled test sets of 100+ records with known-correct extractions. Precision and recall measured per field type. CI fails if accuracy drops below threshold on any prompt change.

How Research Triangle engagements start.

For extraction and RAG pipelines, we start with sample documents: 10 to 20 representative papers or research reports. We assess format complexity, identify the hard cases, and choose the appropriate extraction approach before quoting. The assessment takes 2 to 4 hours and is included in the discovery call process.

For enterprise prompt registry and evaluation CI work at SAS or Red Hat, the discovery call reviews your current prompt storage approach, the LLM features in production, and your existing CI infrastructure. We scope a phased approach: prompt registry migration first, evaluation CI second, if that sequence makes sense for your release cycle.

Raleigh engagements can be conducted remotely or with on-site working sessions at your Durham, Raleigh, or Cary office.

Technical questions from Research Triangle teams.

How does SPECTER2 improve retrieval for a large academic corpus at NC State or Duke?: Standard embedding models (OpenAI text-embedding-3-large, for instance) were trained on general web text. SPECTER2 was trained specifically on scientific papers and their citation relationships, which means its embedding space captures domain proximity in scientific literature more accurately than a general model. For a retrieval-augmented pipeline over a corpus of 50,000 biomedical papers at a UNC or Duke research library, SPECTER2 retrieves more relevant papers for a given query than a general embedding model, particularly for queries that use field-specific terminology that appears rarely in general text. The performance gap is largest in specialized subfields.
What does prompt management at enterprise scale look like for SAS Institute?: For a SAS Institute analytics product with LLM features used by thousands of customers, prompt management means: prompts are checked into a Git repository as first-class code artifacts with the same review process as application code. Each prompt has a version identifier that the application logs with every LLM call. A CI pipeline runs the evaluation suite against every prompt PR before merge. A prompt registry service maps feature names to the currently active prompt version and supports instant rollback to a previous version without a full deployment. Environment variables are for secrets, not prompts.
How does two-pass table extraction work for NC State Agricultural and Life Sciences research papers?: Agricultural research papers have complex tables: multi-level column headers, merged cells, units in footnotes, and superscript statistical significance markers. Standard LLM prompting on the raw PDF text frequently misassigns values to incorrect columns or misses footnote references. Two-pass extraction works like this: Pass one uses pdfplumber to extract the raw table structure, including cell positions, merged cell spans, and footnote identifiers. Pass two passes the structured table representation (a JSON with explicit column-row mappings) to the LLM along with the footnote text. The LLM extracts values with their column context already resolved, not by reading linear PDF text.
What does a confidence score system look like for a Red Hat research extraction pipeline?: For Red Hat's enterprise software, a research extraction confidence system has three layers. Model confidence: the LLM's softmax probability on its top extraction answer, where available. Rule-based validation: format checks (is the extracted version number in the expected format?), range checks (is the benchmark score within the expected range for this test type?), and cross-field consistency (does the cited GitHub commit hash exist in the repository?). Historical baseline: if the same field has been extracted from 50 similar documents previously, does this value fall within two standard deviations of historical values? Records flagging on any layer go to a review queue.

Ready to build the production infrastructure around your LLM pipeline?

Describe the specific problem: corpus type, extraction task, or current infrastructure gap. We will reply with a technical assessment within one business day.

Hire an LLM engineer in Raleigh.

Fixed-price engagements, scoped after a technical assessment of your corpus and infrastructure.

Four technical problems the Research Triangle runs into.

General embeddings underperform on scientific corpora

Table extraction from research papers fails with naive prompting

Prompts treated as configuration strings, not code artifacts

Research extraction without confidence scores

What we build for Research Triangle clients.

SPECTER2 embedding pipeline

Domain-adapted embeddings for scientific paper corpora. Higher retrieval precision for RAG over biomedical, agricultural, and computer science literature.

Two-pass table extraction

pdfplumber for table structure, LLM for value extraction with structure as context. Handles multi-level headers, merged cells, and footnote-referenced units.

Git-based prompt registry

Prompts as versioned TypeScript or Python functions. CI evaluation on every prompt PR. Registry service for feature-to-version mapping and instant rollback.

Multi-layer confidence scoring

Model probability, rule-based validation (format, range, cross-field consistency), and historical baseline comparison. Records flagging on any layer route to review queue.

RAG pipeline with domain embeddings

Retrieval-augmented generation over large scientific corpora using SPECTER2 for indexing and retrieval, GPT-5 or Claude for generation with cited sources.

Evaluation CI for research pipelines

Labeled test sets of 100+ records with known-correct extractions. Precision and recall measured per field type. CI fails if accuracy drops below threshold on any prompt change.

How Research Triangle engagements start.

Raleigh engagements can be conducted remotely or with on-site working sessions at your Durham, Raleigh, or Cary office.

Technical questions from Research Triangle teams.

How does SPECTER2 improve retrieval for a large academic corpus at NC State or Duke?

Standard embedding models (OpenAI text-embedding-3-large, for instance) were trained on general web text. SPECTER2 was trained specifically on scientific papers and their citation relationships, which means its embedding space captures domain proximity in scientific literature more accurately than a general model. For a retrieval-augmented pipeline over a corpus of 50,000 biomedical papers at a UNC or Duke research library, SPECTER2 retrieves more relevant papers for a given query than a general embedding model, particularly for queries that use field-specific terminology that appears rarely in general text. The performance gap is largest in specialized subfields.

What does prompt management at enterprise scale look like for SAS Institute?

For a SAS Institute analytics product with LLM features used by thousands of customers, prompt management means: prompts are checked into a Git repository as first-class code artifacts with the same review process as application code. Each prompt has a version identifier that the application logs with every LLM call. A CI pipeline runs the evaluation suite against every prompt PR before merge. A prompt registry service maps feature names to the currently active prompt version and supports instant rollback to a previous version without a full deployment. Environment variables are for secrets, not prompts.

How does two-pass table extraction work for NC State Agricultural and Life Sciences research papers?

Agricultural research papers have complex tables: multi-level column headers, merged cells, units in footnotes, and superscript statistical significance markers. Standard LLM prompting on the raw PDF text frequently misassigns values to incorrect columns or misses footnote references. Two-pass extraction works like this: Pass one uses pdfplumber to extract the raw table structure, including cell positions, merged cell spans, and footnote identifiers. Pass two passes the structured table representation (a JSON with explicit column-row mappings) to the LLM along with the footnote text. The LLM extracts values with their column context already resolved, not by reading linear PDF text.

What does a confidence score system look like for a Red Hat research extraction pipeline?

For Red Hat's enterprise software, a research extraction confidence system has three layers. Model confidence: the LLM's softmax probability on its top extraction answer, where available. Rule-based validation: format checks (is the extracted version number in the expected format?), range checks (is the benchmark score within the expected range for this test type?), and cross-field consistency (does the cited GitHub commit hash exist in the repository?). Historical baseline: if the same field has been extracted from 50 similar documents previously, does this value fall within two standard deviations of historical values? Records flagging on any layer go to a review queue.

Ready to build the production infrastructure around your LLM pipeline?

Describe the specific problem: corpus type, extraction task, or current infrastructure gap. We will reply with a technical assessment within one business day.