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Python Flask Developer Remote Jobs in Naperville, IL

CoSourcing Partners

GCP Gemini AI Developer

Chicago, IL · On-site +1

Remote / Hybrid - Chicago preferred Employment Type: Contract / Full-Time Reports To: GCP Technical ... Python or TypeScript (Google Cloud SDKs, google-generativeai, aiplatform) * FastAPI / Flask ...

Xlysi

AI Engineer | Remote | W2 Only |

Chicago, IL · Remote

We're looking for an experienced software engineer with strong Python expertise who is passionate about AI and building scalable enterprise solutions. This role requires a strong engineering ...

Performacentric

Full Stack Developer

Chicago, IL · On-site +1

Full Stack Developer (React, Python & FastAPI) Location: Remote (Preferred: Philippines, Latin America, or North America) Employment Type: Full-Time / Contract Company: Performacentric About ...

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Python Flask Developer Remote information

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How much do python flask developer remote jobs pay per hour?

As of Jun 24, 2026, the average hourly pay for python flask developer remote in Naperville, IL is $58.53, according to ZipRecruiter salary data. Most workers in this role earn between $48.27 and $66.49 per hour, depending on experience, location, and employer.

What is the difference between Python Flask Developer Remote vs Python Django Developer Remote?

AspectPython Flask Developer RemotePython Django Developer Remote
Required CredentialsProficiency in Flask, Python, REST APIs, basic database knowledgeProficiency in Django, Python, REST APIs, database management
Work EnvironmentRemote, collaborative teams, startup or tech companiesRemote, often larger organizations or SaaS providers
Industry UsageWeb development, microservices, lightweight APIsFull-stack web apps, enterprise solutions, content management
Search & Comparison IntentCommonly compared due to similar Python frameworks and remote work options

Both Python Flask Developer Remote and Python Django Developer Remote roles involve remote work with Python expertise. Flask developers typically focus on lightweight, flexible microservices, while Django developers work on more comprehensive, full-featured web applications. The choice depends on project complexity and specific framework familiarity.

What are the key skills and qualifications needed to thrive as a Python Flask Developer in a remote role, and why are they important?

To excel as a remote Python Flask Developer, you need strong proficiency in Python programming, experience with the Flask web framework, and a solid understanding of RESTful API design, usually supported by a relevant degree or portfolio. Familiarity with tools such as Git, Docker, cloud platforms (e.g., AWS), and CI/CD pipelines is highly valued, alongside knowledge of databases like PostgreSQL or MongoDB. Excellent problem-solving, self-motivation, and clear written communication are crucial soft skills for effective remote collaboration and project delivery. These skills ensure the developer can build robust web applications, work independently, and contribute efficiently to distributed teams.

What are the main collaboration and communication tools used by remote Python Flask developers, and how do they impact daily workflow?

Remote Python Flask developers typically rely on tools like Slack or Microsoft Teams for instant messaging, Zoom or Google Meet for virtual meetings, and project management platforms such as Jira or Trello for tracking tasks. Version control is managed using Git services like GitHub or GitLab. These tools facilitate seamless collaboration with cross-functional teams, help maintain transparency, and ensure that everyone stays aligned on project goals, making remote work efficient and organized.

What does a Python Flask Developer do when working remotely?

A Python Flask Developer working remotely designs, develops, and maintains web applications using the Flask framework and the Python programming language. Their responsibilities often include building and integrating APIs, writing backend logic, managing databases, and collaborating with team members through digital communication tools. Remote developers also ensure code quality through testing and debugging, while keeping up with project requirements and deadlines. Effective communication and self-motivation are essential for success in a remote role.
What are the most commonly searched types of Python Flask Developer jobs in Naperville, IL? The most popular types of Python Flask Developer jobs in Naperville, IL are:
What cities near Naperville, IL are hiring for Python Flask Developer Remote jobs? Cities near Naperville, IL with the most Python Flask Developer Remote job openings:
Python Flask Developer Remote jobs near you
GCP Gemini AI Developer

GCP Gemini AI Developer

CoSourcing Partners

Chicago, IL • On-site, Remote

Other

Posted 21 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

Apply