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Remote Fastapi Developer Jobs in Chicago, IL (NOW HIRING)

CoSourcing Partners

GCP Gemini AI Developer

Chicago, IL · On-site +1

Remote / Hybrid - Chicago preferred Employment Type: Contract / Full-Time Reports To: GCP Technical ... FastAPI / Flask / Node.js * LangChain / LlamaIndex for orchestration * SQL, Pandas, and Jupyter for ...

EPAM Systems Inc

Lead Python Developer

Chicago, IL · Remote

$120K - $200K/yr

Experience with FastAPI or other Python frameworks strongly desired * Proficiency with AWS ... Remote Position Cannot be Performed in New York City. This posting includes a good faith range of ...

MBR Partners

Senior Software Engineer

Chicago, IL · Remote

$150K/yr

... FastAPI Experience with React, Redux (RTK), CSS modules Experience with CI/CD, Clouds (AWS/GCP ... remote role, ideally US or Europe ( NO Applications from India / Pakistan ) EST Timezone

New

Miller Cooper

Full Stack Software Engineer

Chicago, IL · Remote

$125K - $145K/yr

Fully remote role that has a large business impact. How You'll Contribute * Design, develop, test ... Contribute to CI/CD pipelines and DevOps processes to ensure efficient deployments and reliable ...

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How much do remote fastapi developer jobs pay per hour?

As of May 28, 2026, the average hourly pay for remote fastapi developer in Chicago, IL is $54.43, according to ZipRecruiter salary data. Most workers in this role earn between $41.59 and $66.63 per hour, depending on experience, location, and employer.

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

To excel as a Remote FastAPI Developer, you need strong proficiency in Python programming, RESTful API design, and experience with the FastAPI framework, typically supported by a relevant degree or equivalent experience. Familiarity with tools such as Docker, Git, SQL/NoSQL databases, and cloud platforms like AWS or Azure is highly valued, and certifications in cloud or backend development can be advantageous. Excellent problem-solving, self-management, and communication skills are crucial for collaborating effectively in a remote environment. These skills ensure you can deliver robust, scalable APIs while efficiently working with distributed teams.

What are some common challenges Remote FastAPI Developers face when collaborating with distributed teams?

Remote FastAPI Developers frequently work with colleagues across different time zones and communication styles, which can make real-time collaboration and code reviews more challenging. Staying aligned on project requirements, API design standards, and deployment schedules often requires proactive communication and thorough documentation. Using tools like version control, issue trackers, and asynchronous messaging helps bridge these gaps, but developers must be disciplined about keeping everyone updated and clarifying technical decisions. Building strong remote working habits and establishing clear processes with your team can greatly improve collaboration and project outcomes.

What is a Remote FastAPI Developer?

A Remote FastAPI Developer is a software engineer who specializes in building web APIs using the FastAPI framework, while working remotely from any location. FastAPI is a modern, high-performance Python web framework used to create APIs quickly and efficiently. Remote FastAPI Developers design, implement, and maintain backend services, typically collaborating with distributed teams through online communication and project management tools. Their responsibilities often include writing clean, scalable code, integrating databases, and ensuring API security and performance.
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GCP Gemini AI Developer

GCP Gemini AI Developer

CoSourcing Partners

Chicago, IL • On-site, Remote

Other

Posted 24 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)OutcomeWhat Success Looks LikeMeasurement1. Gemini-Powered Solutions DeployedDesign, 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 ArchitectureBuild 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 WorkflowsImplement 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 EnablementPartner 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 OptimizationMonitor, 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

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