David Tran - DevOps Engineer

Overview

Backend engineer (Senior) focused on robust Python-based ingestion and API backends with extensive, edge-case driven tests. The developer's strongest proven skill is building reliable data ingestion and normalization pipelines, demonstrated by the ingest sources (house_clerk.py, senate_efd.py), parser (ingest/parser.py), store_filing logic (ingest/store.py) and a broad test suite validating edge cases. There is limited public evidence of large-scale distributed operations (message brokers, horizontal worker orchestration), production authn/authz flows, or formal rate-limiting and secrets-management patterns.

Timeline

Software Developer II · Middle

ProPath · Full-Time

Oct 2021 to Aug 2026 4 Years 10 Months Dallas In office

Built and maintained business-critical laboratory systems at a nationwide pathology organization, focusing on production applications, APIs, integrations, and workflow automation. Led a two-phase migration of large-scale production database data with controlled downtime and validated integrity. Developed internal shared infrastructure for multiple applications and work queues, and mentored junior developers through technical guidance.

PHP

JavaScript

MySQL

MariaDB

SQL

Software Developer · Middle

ProPath · Full-Time

Jul 2017 to Oct 2021 4 Years 3 Months Dallas In office

Developed and enhanced production systems and internal tools for laboratory operations, including APIs, integrations, and workflow automation. Contributed to application and data workflows supporting large-scale case processing and reduced manual data entry. Collaborated with laboratory stakeholders to translate operational processes into reliable software solutions.

PHP

JavaScript

MySQL

MariaDB

SQL

University of Texas at Austin

Non-Degree Program · Health Informatics and Health Information Technology (HIHIT)

2015 Austin, Texas

University of Texas at Dallas

Bachelor's Degree · Biology

2008 Dallas, Texas

Projects

Smart Document

Created an OCR and RAG document pipeline that extracts text from uploaded documents, builds searchable embeddings, and supports question answering. Implemented REST endpoints for ingestion and querying, using LangChain with an Ollama-backed LLM and a vector store for retrieval. Containerized the service for reproducible deployment.

Congressional Stock Tracker

Built a full-stack cloud app to ingest, normalize, and present U.S. congressional stock trades using a FastAPI backend and a PostgreSQL database. Implemented scheduled ingestion and PDF parsing with automated error handling and API-driven retrieval. Added observability with OpenTelemetry and Grafana Cloud, containerized and deployed to AWS, and set up CI/CD with security scanning.

Technical skills

Languages

4

SQL

JavaScript

PHP

Python

4

FastAPI

Pydantic

HTTPX

Uvicorn

Databases

3

MySQL

MariaDB

PostgreSQL

DevOps

4

OpenTelemetry

GitHub

AWS

Amazon EC2

AI/ML

5

Sentence-Transformers

LLM

LangChain

Transformers

RAG

Other

22

Pytest

Vitest

Amazon EventBridge

GitHub Actions

Grafana

Containers

Ollama

Docker

Git

Linux

Windows

Rest API

OCR

NLP

CI/CD

AI Agents

CodeQL

Dependabot

TCP/IP

Embeddings

ETL/ELT

Tesseract OCR

Senior AI/ML Engineer Confidence: High LLM Engineer

Senior backend and data-engineering focused LLM engineer specializing in robust document ingestion, normalization, and read-time signal computation. The strongest proven skill is reliable PDF parsing and ingestion with end-to-end provenance and many edge-case tests, as shown by politician_dashboard/ingest/parser.py, the ingest sources (ingest/sources/*), store_filing, and the comprehensive tests under tests/*. Public code does not show custom model training, experiment tracking, or low-level ML performance engineering.

Model Architecture & Training

2/10

How well models are designed and trained

Minimal custom model architecture or training; uses off-the-shelf embedding models and local LLM inference but no custom training loops, optimizer/scheduler choices, or reproducible experiment harness.

Evidence

smart-document/smart_doc.py: uses SentenceTransformer for embeddings and ollama for local model inference

smart-document/requirements.txt: sentence-transformers and ollama present

Data Pipeline & Feature Engineering

7/10

How data is prepared for models

Strong data engineering and ingestion design with careful parsing, normalization, idempotency, provenance and extensive unit/integration tests around many edge cases.

Evidence

politician-dashboard/ingest/parser.py: detailed PDF parsing and normalization functions

politician-dashboard/ingest/sources/house_clerk.py and ingest/sources/senate_efd.py: robust source adapters with HTTP handling and normalization

politician-dashboard/ingest/store.py and migrations/migrate.py: storage persistence and migration tooling

Experimentation & Evaluation

2/10

How results are measured and tested

Some structured testing and validation of AI-driven components, but no experiment tracking, systematic ablations, or ML-focused reproducibility stack.

Evidence

politician-dashboard/tools/ai_review/tests/*: unit tests for the AI-review pipeline components

tests/*: extensive unit and integration tests validating parsing and ingestion behavior

MLOps & Deployment

3/10

How models are shipped to production

Some deployment and observability work (OpenTelemetry instrumentation, API, migrations), and local RAG serving patterns; not a mature MLOps stack for model lifecycle/versioning or drift monitoring.

Evidence

politician-dashboard/observability.py: OpenTelemetry instrumentation wiring for FastAPI and DB

politician-dashboard/api/main.py: FastAPI application factory and lifecycle handling

Computational Efficiency

1/10

How efficiently computing resources are used

No evidence of GPU/quantization/distributed training or profiling; embeddings and local inference used without measured efficiency engineering.

Evidence

smart-document/smart_doc.py: uses sentence-transformers and local Ollama client but no GPU profiling or quantization code

Research Depth & Innovation

1/10

Depth of research and new ideas

No custom research contributions, novel layers, or paper reproductions; engineering focuses on robust production-quality glue and domain logic.

Evidence

politician-dashboard/ingest/* and tools/ai_review/*: solid engineering but no custom model research artifacts

Expertise

RAG· Middle

Document Intelligence & OCR· Senior

LLM· Middle

Technologies

SQL· since 2017 · Middle

MySQL· since 2017

PostgreSQL

LangChain

Sentence-Transformers

GitHub Actions

Embeddings

AI Agents

NLP

Ollama

CI/CD

Transformers

Git

MariaDB· since 2017

AWS

Docker

Grafana

LLM

RAG

Tesseract OCR

Amazon EC2

OCR

Amazon EventBridge

Linux

Windows

TCP/IP

LLM· mentioned only

Recommendations

Build production-grade document intelligence and RAG features that need robust parsing, chunking, and retrieval (implement/read improvements to smart-document/smart_doc.py).
Develop ETL pipelines and ingestion connectors for regulated public-data sources requiring provenance, idempotency and robust identity resolution (extend ingest/sources/* and store_filing flows).
Implement LLM evaluation and guardrail tooling for hosted/local models (improve tools/ai_review reviewer and add reproducible model-run logging and metrics).
Work on API-backed analytics and signal computation services with strong observability and migrations (extend politician_dashboard/api and observability instrumentation).

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

politician-dashboard smart-document devassist

Senior Backend Developer Confidence: High API Engineer

Backend engineer (Senior) focused on robust Python-based ingestion and API backends with extensive, edge-case driven tests. The developer's strongest proven skill is building reliable data ingestion and normalization pipelines, demonstrated by the ingest sources (house_clerk.py, senate_efd.py), parser (ingest/parser.py), store_filing logic (ingest/store.py) and a broad test suite validating edge cases. There is limited public evidence of large-scale distributed operations (message brokers, horizontal worker orchestration), production authn/authz flows, or formal rate-limiting and secrets-management patterns.

API Design

6/10

How well APIs are designed

API design is deliberate and well-tested with clear schemas, consistent error envelopes, pagination and sorting, and route-level validation using Pydantic and FastAPI.

Evidence

politician_dashboard/api/schemas.py: Pydantic models and ok envelope

politician_dashboard/api/routes/*.py: route definitions with Query/Depends and validation (e.g. routes/politicians.py, routes/signals.py)

tests/test_api.py: end-to-end API behavior, pagination, sorting and error handling tests

Data Layer & Database

6/10

Working with databases

Data layer shows explicit migration tooling, transactional store behavior, uniqueness and rollback tests, and hand-authored SQL/query helpers and integrity-focused tests.

Evidence

politician_dashboard/migrations/migrate.py: migration runner and idempotency tests

politician_dashboard/ingest/store.py: store_filing implementation and StoreError handling

tests/test_store.py & tests/test_migrations.py: transactional uniqueness/rollback and migration tests

Scalability & Performance

4/10

Handling load and speed

Some scalability and performance work is present (DB connection pooling and read-time cluster computations), but no wide-area queueing, measured load testing, or explicit cache invalidation strategy is implemented.

Evidence

politician_dashboard/api/db.py: create_pool and connection_dependency (psycopg_pool usage)

politician_dashboard/api/queries.py & signal computation tests: read-side clustering logic (signals) exercised in tests

System Architecture

5/10

Overall system structure

Clear module boundaries (ingest, sources, api, migrations, observability) and deliberate design choices for ingestion adapters and test harnesses support maintainable service decomposition, though this is monorepo-stage rather than a fully distributed microservices deployment.

Evidence

politician_dashboard/ingest/sources/*: multiple source adapters with clear interfaces (house_clerk.py, senate_efd.py)

politician_dashboard/ingest/runner.py: pipeline orchestration and runner harness

politician_dashboard/api/main.py & politician_dashboard/observability.py: app lifecycle and instrumentation hooks

Security & Auth

3/10

Protecting data and access

Basic boundary security practices are present (input validation via Pydantic, explicit error types), but there is limited evidence of authn/authz, token lifecycle, secrets management, or threat-specific mitigations (SSRF, injection audits) in the code inspected.

Evidence

politician_dashboard/api/schemas.py: Pydantic models for request/response validation

politician_dashboard/api/errors.py: structured API error types

politician_dashboard/config.py: get_database_url reads from environment (basic secrets surface)

Reliability & Observability

6/10

Stability and monitoring

Observability and reliability are well-considered with OpenTelemetry instrumentation, structured logging hooks, comprehensive testing of failure modes in ingestion, and run accounting in the runner; graceful-shutdown and retry backoff patterns are present at pipeline level though not uniformly across all external calls.

Evidence

politician_dashboard/observability.py: OpenTelemetry instrumentation and configure_logging

politician_dashboard/api/main.py: lifespan and instrumentation of FastAPI app

tests/test_observability.py & tests/test_runner.py: tests asserting telemetry and runner accounting/partial-failure behavior

Expertise

Python· Senior

Microservices & API Architecture· Senior

Databases & Vector Storage· Middle

Backend AI & LLM· Middle

Industries

Government· Senior

Financial Services· Middle

Technologies

PHP· since 2017 · Middle

Rest API

FastAPI

HTTPX

Uvicorn

Recommendations

Own end-to-end ingestion/ETL and API backends that require careful source parsing and data integrity (extending source adapters and parser robustness).
Build production-grade ingestion scalability: add a queue/worker layer (e.g. Celery/Kafka) and materialized or eventual workers to decouple downloads and parsing from web request surfaces.
Harden production ops: add secrets management, explicit auth layers for the API, documented runbooks, and automated observability alerts and load tests.
Evolve DB schema management with a migration history tool (Alembic-style) and explicit schema evolution tests if the service grows to multiple writers or sharded storage.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

politician-dashboard neetcode-submissions smart-document

Senior DevOps Engineer Confidence: Medium Platform SRE

Platform-focused Python engineer (senior) specializing in reliable data ingestion and observability-driven services. The strongest proven skill is robust, well-tested ingestion and parsing pipelines with OpenTelemetry instrumentation, demonstrated by politician_dashboard/ingest/runner.py, the House/Senate source adapters, and politician_dashboard/observability.py with corresponding tests. There is limited evidence of production-grade CI/CD, explicit IaC modules, Kubernetes orchestration, or cloud-cost optimization automation in the human-authored code.

CI/CD Pipelines

2/10

Automated build and deploy

Minimal CI/CD evidence: repository contains container build artifacts (multi-stage Dockerfiles) and code that integrates with GitHub APIs, but no reusable workflows, gated deploys, artifact signing, or advanced pipeline engineering patterns are present.

Evidence

politician-dashboard/Dockerfile: multi-stage Python build and runtime stages

politician-dashboard/dashboard/Dockerfile: Node multi-stage build and nginx runtime

politician-dashboard/tools/ai_review/github_client.py: GitHub API client implementation

Infrastructure as Code

1/10

Managing servers with code

Very light Infrastructure-as-Code footprint in human-authored code; database migrations and a migration runner exist, but there are no versioned Terraform/Pulumi modules, remote state/locking, state migrations, or environment-separated IaC patterns.

Evidence

politician-dashboard/politician_dashboard/migrations/migrate.py: migration discovery and runner

politician-dashboard/politician_dashboard/config.py: get_database_url reading environment

Containerization & Orchestration

4/10

Working with containers

Good containerization basics: multi-stage Dockerfiles, non-root runtime user, and separate frontend/backend images; no Kubernetes manifests, resource tuning, probes, PDBs, or GitOps manifests were found.

Evidence

politician-dashboard/Dockerfile: multi-stage build, creates non-root 'app' user and sets CMD to uvicorn

politician-dashboard/dashboard/Dockerfile: multi-stage Node build and nginx runtime

Observability & Monitoring

6/10

Watching system health

Strong observability focus: OpenTelemetry instrumentation and SDK wiring are implemented for traces, metrics, and logs, and there are unit/integration tests validating telemetry behavior.

Evidence

politician-dashboard/politician_dashboard/observability.py: OpenTelemetry instrumentation, log/metric exporters, FastAPI instrumentation

politician-dashboard/tests/test_observability.py: tests exercising logging/trace/metric export plumbing

Reliability & Incident Response

5/10

Keeping systems up

Clear reliability engineering: idempotent ingestion runner, explicit run accounting, robust error types and handling, and extensive hermetic tests simulating failures and idempotency; however there is no explicit runbook/chaos evidence or SLO/burn-rate alerting encoded as code.

Evidence

politician-dashboard/politician_dashboard/ingest/runner.py: run_senate_ingestion / runner accounting and failure metrics

politician-dashboard/tests/test_runner.py and politician-dashboard/tests/test_senate_ingestion.py: tests covering idempotency, skip-existing-doc logic, and error/partial-failure handling

Cloud & Cost Optimization

1/10

Smart use of the cloud

Minimal cloud/cost-optimization artifacts in human-authored code; configuration reads database URL from the environment and Dockerfiles support container deployment, but there are no autoscaling, spot/eviction, rightsizing, or IAM workload-identity patterns present.

Evidence

politician-dashboard/politician_dashboard/config.py: get_database_url reads from environment

politician-dashboard/Dockerfile: production-focused runtime image and non-root user

Expertise

Observability & Monitoring· Senior

Site Reliability Engineering· Senior

Platform Engineering & IDP· Middle

Industries

Government· Senior

Technologies

Containers

Python· Senior

OpenTelemetry

Pydantic

GitHub

Recommendations

Develop production CI/CD pipelines (reusable GitHub Actions or Buildkite flows) that implement gated deploys, image signing, and deploy rollback gates tied to run metadata
Add IaC (Terraform or CDKTF) with remote state and locking plus environment separation to codify deployments, networking, and DB provisioning
Introduce k8s manifests or GitOps (ArgoCD/Flux) with tuned resource requests/limits, liveness/readiness probes, and PodDisruptionBudget for the API and workers
Expand observability-as-code: define SLOs, burn-rate alerts, and alert routing/inhibition rules (as code) tied to the existing OpenTelemetry metrics and traces

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

politician-dashboard devassist

Job preferences

Work permits: United States
Citizenship: United States
Salary: from $100k per year
Employment type: Full-Time
Locations: Dallas, New York