Green Bay, WI · open to work

Vector search, embeddings, and the infra underneath.

I'm James Christensen — a database platform engineer who ships production retrieval on PostgreSQL + pgvector, and writes the AWS CDK that provisions it. Embedding pipelines, hybrid search, HNSW indexes, 400+ managed instances.

See the workDownload résumé
embedding space · 1024d → 3ddrag to rotate · hover a vector0 vectors
pgvector / retrievalPostgres platformAWS CDK / infradelivery / leadership
400+
RDS instances managed
1st
Postgres over Oracle as the standard
3
CDK constructs shipped enterprise-wide
50
Lambdas running in production
0
P0 incidents
01 / selected work

Retrieval systems, shipped.

Try the search below — it scores my projects against your query the same way a vector index would, then lights up the matching region of the cloud above.
>
Multi-tenant SaaS · flagship

clrframe

similarity
0.720
clrframe · product walkthroughvideo → transcript → draft SOP

A document hub for companies that also writes the documentation. Record a narrated video walkthrough of a process and clrframe transcribes it, pulls out the steps, and turns it into a draft SOP — so the knowledge in someone’s head becomes a document without anyone sitting down to write one. Photos, videos, and existing files all land in one searchable library: every upload is extracted, chunked, and embedded into PostgreSQL with pgvector, retrieval fuses full-text and vector similarity with Reciprocal Rank Fusion, and a read-only agent mode researches the library and proposes new SOPs with cited sources. ~50 Lambdas across 13 nested CDK stacks in three AWS accounts, Step Functions ingestion, 98%+ enforced backend coverage.

PostgreSQLpgvectorAmazon BedrockRAGRRFVideo → SOPAWS CDKStep FunctionsAngular
GitHub ↗clrframe.com ↗
Open-source demo

pgvector RAG Demo

similarity
0.640

A one-command demo that makes embeddings visible: upload a document, watch each chunk embed live into Postgres behind an HNSW index, see the 1024-dimensional vectors projected to 2D, then ask questions with RAG on or off — retrieved chunks light up in vector space. Runs fully local with GPU models via Docker Model Runner.

PostgreSQLpgvectorHNSWEmbeddingsFlaskAngularDocker
GitHub ↗
Enterprise hackathon · led

ClaimLens

similarity
0.580

AI research aid for insurance claims. Historical claims are vectorized into Postgres so a claim number — or a plain-language scenario — returns the most similar past claims, replacing hours of manual research and helping newer associates learn from real precedent.

PostgreSQLpgvectorSemantic SearchEmbeddings
Client production site

VanderLeest Trailers

similarity
0.580

Production site for a Northeastern Wisconsin trailer dealership, replacing WordPress. Angular SPA with a Cognito-backed admin CMS so the owner runs content and inventory alone, plus a Bedrock sales assistant that tool-calls live DynamoDB inventory and captures leads. Single CDK stack with per-route throttles and edge caching in front of the LLM endpoints.

AngularAmazon BedrockAWS CDKDynamoDBLambdaCognito
GitHub ↗vanderleesttrailers.com ↗
Enterprise platform

Postgres CDK Construct

similarity
0.580

The construct that made PostgreSQL a first-class managed offering at a Fortune 100 insurer: RDS PostgreSQL and Aurora provisioned from a single CDK construct with IAM-driven automation for schema and role creation, per-instance CloudWatch alarms wired to BigPanda, and auto-created ServiceNow incidents. Now backing 400+ instances.

AWS CDKRDSAuroraIAMCloudWatchGitHub Actions
02 / experience

Five years, one Fortune 100 platform.

Jan 2025 — Present
Nationwide

Consultant Database Platform Engineer

  • →Led the Tiger Team that standardized Postgres and Aurora across the enterprise, shaping long-term RDBMS strategy.
  • →Elevated PostgreSQL to a first-class managed service with an AWS CDK construct for RDS/Aurora and IAM-driven schema and role automation.
  • →Managed 400+ RDS instances with per-instance CloudWatch alarms wired through BigPanda into auto-created ServiceNow incidents.
  • →Designed and led OpenSearch and Neptune CDK constructs, and migrated repos from Concourse to GitHub Actions.
Jan 2023 — Jan 2025
Nationwide

Specialist Software Engineer

  • →Led production support to operational excellence through automation — zero P0 incidents across the period.
  • →Built a SOAPUI testing pipeline that automated regression for multiple lines of business.
  • →Extended into requirements analysis, plus project, release, and environment management as needed.
Feb 2022 — Jan 2023
Nationwide

Senior Software Engineer

  • →Took over as Lead of the ODM production support area and moved monitoring and observability onto BigPanda.
  • →Cut production support resourcing 50% through pipeline automation in Concourse, Jenkins, Artifactory, and Harness.
  • →Voted Lead of the Automation Guild Pilot Hackathon; presented automation results to Nationwide managers.
Jun 2021 — Feb 2022
Nationwide

Software Engineer

  • →Shrank the test case repository 40% with automated cleanup scripting, and caught invalid-data cases that had gone undetected for years.
  • →Built a GUI that automated prerelease test-case merging, and a Jira↔GitHub bot that moved cards and linked pull requests automatically.
Jun 2020 — Jun 2021
Nationwide

Intern

  • →Delivered a full-time associate workload part-time while finishing a CS degree.
03 / how it works

Document in, cited answer out.

The retrieval path I build and operate — every stage lives in Postgres or a CDK-provisioned AWS service.

01
Ingest

Uploads land in S3 and fan out through Step Functions — no request blocks on extraction.

S3 · Step Functions · Lambda
02
Chunk

Documents are extracted and split with overlap, keeping citations back to page and position.

Textract · Python
03
Embed

Each chunk becomes a 1024-dimensional vector and is written straight into Postgres.

Bedrock Titan · pgvector
04
Index

HNSW for vectors, tsvector for lexical — one table, two access paths, one transaction.

HNSW · GIN · SQL
05
Retrieve

Vector and full-text results are fused with RRF, then answered by an LLM with sources cited.

RRF · Bedrock · agents
hybrid retrieval · rrf over pgvector + tsvector
WITH semantic AS (
  SELECT id, RANK() OVER (ORDER BY embedding <=> $1) AS rank
  FROM chunks WHERE tenant_id = $2
  ORDER BY embedding <=> $1 LIMIT 40
),
lexical AS (
  SELECT id, RANK() OVER (ORDER BY ts_rank_cd(tsv, query) DESC) AS rank
  FROM chunks, websearch_to_tsquery('english', $3) query
  WHERE tenant_id = $2 AND tsv @@ query LIMIT 40
)
SELECT c.id, c.content, c.source_page
FROM semantic s FULL JOIN lexical l USING (id)
JOIN chunks c USING (id)
ORDER BY COALESCE(1.0/(60 + s.rank), 0) + COALESCE(1.0/(60 + l.rank), 0) DESC
LIMIT 8;
04 / stack

What I reach for.

Databases & vector search
PostgreSQLpgvectorAmazon RDSAmazon AuroraSQLHybrid search (FTS + RRF)HNSW indexing
AI & retrieval
Amazon BedrockRAG pipelinesEmbeddingsSemantic searchAgent tool usePrompt engineering
Cloud & IaC
AWS CDKLambdaStep FunctionsAPI GatewayCloudFrontCloud securityObservability
Delivery & leadership
CI/CD automationGitHub ActionsPythonAngularNode.jsMentorshipCross-team enablement
University of Iowa
Education
University of Iowa
BS Computer Science · GPA 3.21
Right now in Green Bay, WI
—fetching current conditions…
GET /api/weather · lambda
Current weather
James Christensen
James Christensen
Green Bay, WI · replies within a day
05 / contact

Let's build something that retrieves.

Postgres platform work, pgvector and RAG builds, or CDK infrastructure — I'd like to hear about it.

© 2026 James ChristensenAngular · AWS CDK · CloudFront · Lambda · Bedrock