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Python Engineers

Hire Python Developers

Data pipelines, AI systems, and production-grade APIs.

Our Python engineers build AI/ML pipelines, data processing systems, and production APIs with Django and FastAPI. From prototype to production, with the rigor your data demands.

Book a strategy callSee relevant workUpdated Mar 2026
35+

Python projects

95%

Model accuracy

3x

Pipeline speed

Overview

Why Python?

1Raft offers Python development teams experienced in AI/ML engineering, data pipelines, and backend API development. Using Django, FastAPI, and modern ML frameworks, 1Raft engineers have built production AI systems, ETL pipelines processing terabytes of data, and APIs serving enterprise clients across healthcare and fintech.

Python is the backbone of our AI and data work. Our engineers use it to build LLM-powered features, computer vision systems, ETL pipelines, and REST APIs. The focus is always on code that runs reliably in production, not just in notebooks.

For APIs, we use FastAPI when speed and async I/O matter, Django when you need batteries-included (auth, admin, ORM). For AI/ML, we work with PyTorch, scikit-learn, LangChain, and custom pipelines built on top of pandas and Polars for data processing.

Our Python teams have built medical imaging analysis systems processing 10,000+ scans daily, recommendation engines that increased average order value by 23%, and ETL pipelines transforming 2TB+ of raw data into analytics dashboards your team can use.

Why 1Raft for Python

  • Django and FastAPI for production-grade web APIs
  • AI/ML: PyTorch, scikit-learn, Hugging Face Transformers
  • LLM integration: LangChain, RAG pipelines, prompt engineering
  • Data engineering: pandas, Polars, Apache Airflow, dbt

Expertise

What our Python team delivers

Django and FastAPI for production-grade web APIs

AI/ML: PyTorch, scikit-learn, Hugging Face Transformers

LLM integration: LangChain, RAG pipelines, prompt engineering

Data engineering: pandas, Polars, Apache Airflow, dbt

Computer vision: OpenCV, YOLO, custom model training

ETL pipeline design and orchestration

Async Python: asyncio, Celery task queues

Testing: pytest, hypothesis, type checking with mypy

Process

How the engagement works

1
Week 1

Data audit and system design

We assess your data setup, define pipeline architecture, and identify the right Python frameworks for your use case. For AI projects, we validate model feasibility before committing to a build plan.

2
Weeks 2-4

Proof of concept

Build the core pipeline or model with real data. For AI projects, this means a working prototype with measured accuracy metrics. For APIs, a deployed staging environment with documented endpoints.

3
Weeks 5-9

Production development

Harden the prototype into production code. Error handling, monitoring, automated testing, and performance optimization. AI models get evaluation suites and drift detection.

4
Weeks 10-12

Deploy and monitor

Production deployment with monitoring, alerting, and documentation. AI systems get model performance dashboards. APIs get load testing results and runbooks.

Use Cases

Real-world Python projects

Medical imaging analysis for a healthcare platform

Built a Python-based computer vision system that analyzes radiology images using custom-trained models. HIPAA-compliant infrastructure with encrypted data handling and audit logging. → Processing 10,000+ scans daily with 94% accuracy. Radiologist review time reduced by 35%. FDA 510(k) submission supported.

Recommendation engine for a commerce platform

Designed a hybrid recommendation system using collaborative filtering and content-based approaches with Python, scikit-learn, and Redis for real-time serving. → Average order value increased 23%. Click-through rate on recommendations hit 12%, up from 3.5% with the previous rule-based system.

ETL pipeline for a fintech data warehouse

Built an automated ETL pipeline with Python, Apache Airflow, and dbt. Ingesting data from 8 sources, transforming 2TB+ daily, and loading into a Snowflake data warehouse. → Data freshness improved from 24-hour lag to 15-minute increments. Analyst self-service queries increased 300%. Pipeline failure rate below 0.5%.

Proof

PythonRemote Patient Monitoring
45%

Readmission reduction

12

Weeks to deploy

Read case study

Technology stack

PythonFastAPIDjangoPyTorchscikit-learnLangChainpandasPolarsApache AirflowCeleryPostgreSQLRedis

Frequently asked questions

Yes. AI and ML work is a core focus. Our Python engineers have built production systems using PyTorch, scikit-learn, Hugging Face, and LangChain. We handle everything from model training and evaluation to deployment and monitoring.

Next Step

Get a Python team matched to your product.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

Talk to a founder
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