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Fastapi Developer Jobs in Bolingbrook, IL (NOW HIRING)

Consumer Reports

Software Engineer - Product

Chicago, IL · On-site +1

$120K - $140K/yr

OVERVIEW We're seeking a passionate Software Engineer to join our Experimental Engineering team ... Implement efficient, scalable APIs and services using Python, FastAPI, and related technologies.

MBR Partners

Senior Software Engineer

Chicago, IL · Remote

$150K/yr

With a back-end team in the Philippines, I'm currently hiring for a Senior Software Engineer ... FastAPI Experience with React, Redux (RTK), CSS modules Experience with CI/CD, Clouds (AWS/GCP ...

Mastech Digital

AI Full Stack Engineer

Chicago, IL · Remote

$70 - $75/hr

AWS Solutions Architect, AWS Developer, or Kubernetes Application Developer. - Hands-on experience ... FastAPI, and NLP frameworks. - Experience deploying applications into AWS EKS and working with ...

Caterpillar

Lead Software Engineer, Gen AI

Chicago, IL · On-site

$128K - $208K/yr

Job Summary: As a Lead Software Engineer you will work on Generative AI projects focused on ... Develop and deploy services using FastAPI on AWS. * Collaborate with data scientists to refactor ...

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How much do fastapi developer jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for fastapi developer in Bolingbrook, IL is $52.25, according to ZipRecruiter salary data. Most workers in this role earn between $39.95 and $63.94 per hour, depending on experience, location, and employer.

What are some common challenges FastAPI Developers face when integrating third-party services or APIs?

FastAPI Developers often encounter challenges when integrating third-party services, such as handling authentication protocols (like OAuth2), ensuring compatibility between JSON schemas, and managing asynchronous calls to avoid performance bottlenecks. It’s also common to troubleshoot and adapt to inconsistencies in external API documentation or rate limits. Collaborating closely with frontend teams and DevOps professionals helps streamline these integrations, ensuring robust, scalable API solutions.

What is the difference between Fastapi Developer vs Backend Developer?

AspectFastapi DeveloperBackend Developer
Required SkillsPython, Fastapi, REST APIs, async programmingMultiple languages (Python, Java, Node.js), REST/SOAP APIs, databases
Work EnvironmentWeb development, API-focused projects, microservicesBroader software development, server-side logic, database management
Industry UsageTech startups, SaaS, API-driven servicesEnterprise, e-commerce, finance, various industries

Fastapi Developers specialize in building high-performance APIs using Python and Fastapi, often within microservices architectures. Backend Developers have a broader scope, working with multiple languages and technologies to develop server-side applications across various industries. While Fastapi Developers focus on API efficiency, Backend Developers handle comprehensive backend systems.

What are the key skills and qualifications needed to thrive as a FastAPI Developer, and why are they important?

To thrive as a FastAPI Developer, you need strong proficiency in Python programming, RESTful API design, and experience with FastAPI, often supported by a background in computer science or related fields. Familiarity with tools like SQL/NoSQL databases, Docker, and cloud platforms, as well as knowledge of asynchronous programming and API documentation tools like Swagger, is typically required. Excellent problem-solving skills, attention to detail, and effective communication set outstanding FastAPI developers apart. These skills are crucial for building reliable, high-performance APIs that meet modern application demands and facilitate seamless team collaboration.

What is a FastAPI Developer?

A FastAPI Developer is a software engineer who specializes in building web applications and APIs using the FastAPI framework, which is a modern, fast (high-performance) web framework for Python. FastAPI Developers are responsible for designing, developing, and maintaining backend services and APIs that are efficient, robust, and scalable. They often work with databases, authentication, and deployment processes, and ensure that the API endpoints adhere to best practices for security and performance. Their work is crucial for enabling smooth communication between front-end applications and backend systems.
What job categories do people searching Fastapi Developer jobs in Bolingbrook, IL look for? The top searched job categories for Fastapi Developer jobs in Bolingbrook, IL are:
What cities near Bolingbrook, IL are hiring for Fastapi Developer jobs? Cities near Bolingbrook, IL with the most Fastapi Developer job openings:
Fastapi Developer jobs near you
GCP Gemini AI Developer

GCP Gemini AI Developer

Co-Sourcing Partners

Chicago, IL • On-site

Full-time

Re-posted 22 days ago

Job description

Job Title: GCP Gemini AI Developer (3-5 Years Experience)
Location: Remote / Hybrid - Chicago preferred
Employment Type: Contract / Full-Time
Reports To: GCP Technical Lead / AI Program Manager
Purpose
The GCP Gemini AI Developer will design, build, and deploy intelligent applications leveraging Google Cloud's Gemini models and Vertex AI platform. This role exists to operationalize advanced GenAI capabilities - including natural language understanding, multimodal reasoning, and generative automation - within scalable, secure, and production-ready cloud environments.
The developer will work hands-on across data engineering, AI model orchestration, and API integration to create AI-driven business solutions that reduce manual effort, enhance decision-making, and unlock measurable value from enterprise data.
Key Performance Outcomes (6-12 Months) Outcome What Success Looks Like Measurement 1. Gemini-Powered Solutions Deployed Design, develop, and deploy at least two Gemini-based AI solutions (e.g., document summarization, chat agent, or data extraction automation) using Vertex AI + Gemini APIs. Delivered to production with >90% accuracy and <2s response time. 2. Scalable Cloud Architecture Build a modular AI microservices framework using Cloud Run / Cloud Functions with integrated authentication, logging, and monitoring. Reusable components adopted in at least 3 future use cases. 3. RAG / Context-Aware Workflows Implement Retrieval-Augmented Generation (RAG) pipelines combining Gemini + BigQuery or vector databases for knowledge grounding. Demonstrated 25% reduction in hallucination or response variance. 4. Cross-Team Enablement Partner with Data, Automation, and AppDev teams to integrate Gemini AI into existing business workflows (e.g., UiPath, Power Platform, or ServiceNow). Minimum of 2 successful integrations with documented ROI. 5. Continuous Optimization Monitor, retrain, and improve AI models via Vertex AI pipelines and Model Monitoring. Demonstrated 15% performance gain over baseline models. Core Responsibilities
  • Design and deploy Gemini 1.5 Pro/Flash integrations via Vertex AI and Generative AI Studio.
  • Build serverless APIs and backend services for AI workflows using Cloud Run, Functions, or App Engine.
  • Develop data ingestion and preprocessing pipelines using BigQuery, Dataform, and Pub/Sub.
  • Apply prompt engineering and parameter tuning to improve generative model accuracy.
  • Implement RAG pipelines leveraging Vertex Matching Engine or Pinecone.
  • Collaborate with automation and data teams to embed AI into existing business processes.
  • Maintain compliance with security, privacy, and model governance standards.

Technical Environment
Core Google Cloud Services
  • Vertex AI, Generative AI Studio, Gemini API
  • BigQuery, BigQuery ML, Dataform
  • Cloud Run, Cloud Functions, Cloud Storage
  • Pub/Sub, Secret Manager, IAM, Cloud Build

Programming Stack
  • Python or TypeScript (Google Cloud SDKs, google-generativeai, aiplatform)
  • FastAPI / Flask / Node.js
  • LangChain / LlamaIndex for orchestration
  • SQL, Pandas, and Jupyter for data prep

Complementary Tools
  • Terraform (IaC)
  • GitHub / GitLab CI/CD
  • Vertex AI Pipelines & Model Registry
  • Vector DB (Vertex Matching Engine, Pinecone, or Weaviate)

Ideal Profile
  • 3-5 years hands-on GCP development experience with AI/ML exposure
  • Strong working knowledge of Vertex AI, Gemini models, and RAG pipeline design
  • Demonstrated ability to move AI prototypes into production
  • Strong communicator, able to collaborate across automation, data, and cloud teams
  • Curious problem-solver passionate about applied AI innovation

Success Metrics
  • Speed to Delivery: End-to-end deployment within 8-10 weeks per use case
  • Model Effectiveness: >90% accuracy or relevance rating from business stakeholders
  • Scalability: Framework reused for ≥3 additional AI initiatives
  • Business Impact: 25%+ improvement in productivity or efficiency from deployed use cases

Apply