Vector search, embeddings, and the infra underneath.

Consultant Database Platform Engineer at NationwideGreen Bay, WI

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.

RDS instances managed
400+
CDK constructs shipped enterprise-wide
3
P0 incidents
0
See the workDownload résumé
illustrative embedding spacedrag · hover a point
  • pgvector / retrieval
  • Postgres platform
  • AWS CDK / infra
  • delivery / leadership
01 / selected work

Retrieval systems, shipped.

Search at the top of the page to rank these. I tagged each project with keywords, and the score is how many of your terms it shares: a stand-in for vector similarity, not a real index.

Multi-tenant SaaS · flagship

clrframe

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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

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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

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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

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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

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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.

  1. Ingest

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

    S3 · Step Functions · Lambda

  2. Chunk

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

    Textract · Python

  3. Embed

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

    Bedrock Titan · pgvector

  4. Index

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

    HNSW · GIN · SQL

  5. 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
James Christensen
Green Bay, WI · replies within a day
05 / contact

Want to talk about any of this?

Roles, projects, or a question about something on this page.

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