Part-Time AI & Full-Stack Help

@OutsourceMan

Hi, CnEL India can support both the time-series forecasting workflow and the ongoing full-stack development of your platform. Our methodology: * Review the historical data, target variable, time-based patterns, and forecasting requirements. * Build a reproducible Python pipeline covering data preparation, feature engineering, training, validation, and iteration. * Evaluate suitable TensorFlow/PyTorch approaches and track model performance with clear metrics. * Keep experiments, configurations, preprocessing, and model versions organized in Git. * Develop secure REST/GraphQL APIs to serve forecasts and accept new data for future retraining workflows. * Integrate the forecasting engine with your existing Node.js + React + PostgreSQL stack. * Build intuitive React views for forecasts, trends, confidence/results, and basic model-performance metrics. * Add validation, authentication/authorization, error handling, logging, and API security. * Test the model pipeline, APIs, data flow, and frontend together. * Maintain clear documentation so the model and platform can be extended later. We can work within the 10–15 hours/week cadence and deliver the work through clearly defined milestones.

@sumitsthakur

I can help you build your time-series prediction pipeline and surface the results through your Node.js and React platform. For the machine learning side, I will construct the supervised learning model from the ground up using clean Python, handling feature engineering, training, validation, and iteration to ensure accuracy and reproducibility. For the full-stack integration, I will develop the necessary endpoints to serve model results and accept new data, while building the front-end components and dashboard views you need to let users explore forecasts in real time. Operating on your desired part-time cadence of 10 to 15 hours a week, we can establish clear milestones for the version-controlled repository, API endpoints, and visualization components. My background aligns well with bridging deep learning workflows and web interfaces. Let's discuss how we can structure the milestones for your project.

@vishal1145

Your time-series model will drift if you don't build a retraining pipeline from day one. Without automated validation checks, you'll deploy predictions that degrade silently as market conditions shift. Quick questions - are you planning to retrain weekly or trigger retraining when prediction error crosses a threshold? And what's your latency requirement for the API serving forecasts? Here is the architectural approach: - PYTORCH + TENSORFLOW: Build LSTM or Transformer-based forecasting pipeline with feature stores for reproducible experiments and A/B testing new architectures against production baselines. - REST API + GRAPHQL: Expose model inference through versioned endpoints with request queuing so forecast generation doesn't block your Node.js event loop under concurrent load. - REACT + DATA VISUALIZATION: Create real-time dashboards using D3 or Recharts that render confidence intervals and let users toggle between historical actuals versus predicted values. I've built similar hybrid ML-web systems for 2 fintech platforms where forecast accuracy directly impacted revenue. Let's schedule a 15-minute call to map your retraining strategy before we write any training code.

@crtvecode

Building a time-series prediction model from scratch requires a solid understanding of both machine learning and full-stack development. I’ve successfully architected those real-time multi-agent AI visualization dashboards, which involved similar workflows, making sure accuracy and reproducibility. My experience at Verisk Analytics also involved creating scalable React solutions, and I think that will be beneficial for developing the front-end components you need. So, I’m looking for a long-term engagement and can commit to the 10–15 hours a week you mentioned, I’m ready to jump in immediately and collaborate on both the model development and the web enhancements. Let’s connect and just get started on this exciting project. Rajavel Python, TensorFlow, React, Node.js Developer

@santoshkumars17

Hi, I’m Santosh, a Full Stack Web & AI Developer with 5+ years of experience in Python, Node.js, React, PostgreSQL, REST APIs, and AI/ML-based applications. I can handle both parts of your project: building the time-series forecasting pipeline and integrating it into your existing web platform. For the ML side, I can develop the workflow from the ground up, including data preprocessing, feature engineering, time-series validation, model training, evaluation, hyperparameter tuning, and reproducible experiments using Python with PyTorch/TensorFlow or suitable ML libraries. I’ll keep the pipeline modular and well documented so the model can be improved and retrained later. For the platform, I can integrate the forecasting engine with your Node.js + React + PostgreSQL stack, build secure REST/GraphQL APIs for predictions and retraining data, and create clean dashboard components for forecasts and performance metrics. I’m comfortable switching between ML development and full-stack implementation and can work within the 10–15 hours/week cadence. I’d be happy to review your existing data and platform structure and discuss the first milestone.

@UzeeTech

With 7+ years across machine learning and full-stack product engineering, I fit both halves of this engagement. For the forecasting engine, I would start from your historical data with exploratory analysis, then build a reproducible Python pipeline covering time-series feature engineering with lags, rolling statistics and seasonality, training and validation with walk-forward splits, and iteration in TensorFlow or PyTorch depending on data scale. The code stays clean and documented, with experiment tracking so accuracy is measured on holdout windows and the model keeps improving. On the platform side, I work daily with Node.js, React and Postgres. I would design versioned, authenticated REST endpoints serving predictions with caching, then build dashboard views with interactive charts letting users explore forecasts in real time, keeping the API contract stable as the model evolves. I own the full loop from data to deployment and ramp on codebases fast. Let us begin with a first milestone covering the data pipeline plus one dashboard view, so you see value quickly.

@aprkdream

Hi, I understand you need a part-time developer to address two interconnected requirements: AI integration and full-stack development. With over 11 years of experience in both domains, I can effectively bridge these needs to deliver robust solutions. For the AI component, I will leverage my expertise in OpenAI and GPT API integration to create intelligent features that enhance user experience. This could include chatbots or automated workflows tailored to your specific business needs. On the full-stack side, I will utilize my proficiency in PHP, Laravel, Node.js, and React to build secure and scalable applications. My approach will ensure seamless integration between the front-end and back-end, optimizing performance and user engagement. Key deliverables include: - AI-powered features that streamline processes and improve user interaction. - A responsive web application with a clean architecture. - Comprehensive API development for smooth data exchange. - Ongoing support and optimization to ensure long-term success. I am committed to delivering high-quality solutions that align with your goals. Let's collaborate to bring your vision to life. Best regards, Anil Prajapati

@akshaybhagwat76

Hi, Your project caught my attention because it combines two areas I genuinely enjoy working on: intelligent forecasting systems and scalable web applications. I have 5+ years of development experience, backed by an experienced team capable of handling both backend engineering and modern frontend development. I understand you're not just looking for a prediction model, but a complete solution where historical data is transformed into meaningful forecasts and presented through a secure, user-friendly dashboard. Here's how I can help: **Machine Learning:** Build a reproducible time-series forecasting pipeline covering data preprocessing, feature engineering, model training, validation, and performance evaluation using Python and suitable ML frameworks. **Backend Integration:** Integrate the forecasting engine with your existing Node.js and PostgreSQL platform through secure REST APIs, including forecast retrieval and controlled data updates for future retraining. **Frontend:** Develop intuitive React dashboards with interactive forecast visualizations, historical comparisons, and model performance metrics. I also understand the importance of clean documentation, version control, maintainability, and measurable milestones. I'm comfortable with the proposed 10–15 hours per week and would be happy to establish a consistent development cadence. Let's turn your historical data into actionable predictions! Best regards, Akshay B.

@UFours

UFours IT Solution Private Limited is a software company in Madurai, India, trusted by clients in India, the UK and Malaysia since 2013. You need forecasts your users can actually trust, and a dashboard that shows them clearly. One team can deliver both: the Python model and the Node.js, React and PostgreSQL platform around it. How we will deliver (10-15 hours per week) Data review: agree target, horizon and accuracy metrics up front. Baseline model first, so every improvement is measurable. Feature engineering, training and walk-forward validation in PyTorch or TensorFlow. Secure REST API to serve forecasts and accept new data for retraining. React dashboard with live forecasts and MAE, RMSE and MAPE metrics. What you get A Git repository with clean, documented code Reproducible results: fixed seeds, pinned dependencies, versioned models A pipeline that is easy to retrain and improve later Clear milestones and a weekly progress update Why UFours 13 years of delivery. No hand-off gap between your model and your dashboard, because one in-house team builds both.

@TechWagger2026

With extensive experience in API and backend development, I'm perfectly suited to handle your multi-faceted project. I have a strong background in Python, including utilizing TensorFlow and PyTorch for robust supervised-learning models that will be ideal for your time-series prediction needs. In fact, I thrive on clean coding practices and thorough documentation as these facilitate improved model iterations which I know you seek. Together we can achieve a version-controlled repo holding the complete time-series prediction pipeline coupled with REST (or GraphQL) endpoints capable of serving model results and accepting new data for retraining. Furthermore, you can count on my ability to build front-end components that not only visualize forecasts but also provide basic performance metrics. My commitment to delivering high-quality work on time combined with an excellent problem-solving approach makes me aptly poised to drive ongoing enhancements in your forecast model as well as your digital platform

@DeMit19

=======> Hi there, <======= I can handle both sides of the project: build a reproducible Python time-series forecasting pipeline with feature engineering, training, validation, and iteration, then integrate the model into your Node.js/React/Postgres platform through secure APIs and dashboard visualizations. I’m comfortable with PyTorch/TensorFlow, REST/GraphQL, React, Node.js, PostgreSQL, and model-serving workflows, so the ML and product layers can evolve together cleanly. Rate : $12/hour Availability : 10–15 hours/week Question: Is the forecasting model expected to retrain automatically on new data, or only through an admin-triggered workflow? Best regards, Dejan

@GobindSawarn

Hi there! I'm Govind, a highly-skilled full-stack developer who is well-versed in the world of AI and machine learning. With hands-on experience in both supervised-learning model development and web platform enhancement, I believe I can tackle your complex project and deliver quality results. Having expertise in Python – a language known for its data processing capabilities – and Web technologies like Node.js, React, and Postgres, I am adept at building solutions end-to-end. This includes the creation of a full-fledged time-series prediction pipeline from feature engineering to training using frameworks like TensorFlow or PyTorch. Additionally, my ability to develop REST (or GraphQL) endpoints to effectively expose your model outputs and my proficiency in handling front-end views that will allow users to interact with the forecasted data are skills that align exceptionally well with your needs. Moreover, my capability to multitask between AI model development areas such as deep learning notebooks and monitoring the performance of Node.js + React web platform makes me suitable for the cadence you require. And as a result-oriented professional, I’ll make sure you receive the deliverables with clean codes, well-documented workflow and most importantly that they serve their purpose accurately. Let's collaborate and transform your data into meaningful insights.

@rajeshrolen

Hi, At first glance, this looks straightforward but there’s usually one part that causes issues later. I’ve handled similar work before and can help you avoid that. Regards, Rajesh

@Visharaa2024

Hi, The combination of a reproducible forecasting pipeline with your existing Node/React/Postgres platform is a strong approach. I’d keep the ML workflow modular so feature engineering, temporal validation, training and model iterations can evolve without turning the forecasting logic into a notebook-only dependency. I’d build the prediction pipeline in Python, expose validated model outputs through a controlled API, and integrate those results into your existing Node.js backend and React dashboard. Forecast views can include historical vs predicted values and relevant model-performance metrics, while retraining remains a controlled, versioned process. I’d avoid committing to a specific deep-learning architecture before reviewing the data structure, seasonality and forecast horizon; those should drive the model and validation strategy. One key question: what is the forecasting target, data frequency and required prediction horizon? If this aligns with your requirements, we can proceed with the initial ML/API architecture and milestones. Best regards, Deepak.

@dushyant317

Hi, we can handle both the time-series ML pipeline and full-stack integration, including feature engineering, model training/validation in Python with TensorFlow/PyTorch, REST/GraphQL APIs, and React dashboards connected to your Node.js/Postgres platform. We can work within your 10–15 hours/week cadence and deliver clean, version-controlled, reproducible code with milestone-based progress.

@bojans9

Hi, Bojan here, from Serbia. What matters most in building your part-time AI and full-stack help with React and GraphQL is a first version real users can sign up for and use, without a rewrite later. I'm a senior full stack developer with 8+ years of experience working with Python, Tensorflow, Pytorch and API Development. With React, the real challenge is auth, roles and data isolation. A common issue: no clear MVP boundary, so it never ships. My plan: first, agree the MVP scope, user roles and data model. Then, build auth, the core flows and the admin side in React. Finally, test roles, permissions and edge cases, then deploy with backups. I've done web app work in Tensorflow and Pytorch, so I check early for data leaking between accounts and roles. A few questions to better understand the scope: 1. Will several companies share one install? Then every query needs a tenant check from the start. 2. Build the admin side on a ready admin framework? I'd recommend it, search, filters and exports come included. 3. Are many users on low-end Android phones? Then I'd keep pages light and test on an entry-level device first. I can start right away. Happy to go over the details here in chat and suggest the best approach. Looking forward to hearing from you, Bojan

@Kishoreindia

Dear Sir/Madam, I am an experienced Python Developer with strong expertise in building scalable backend systems, APIs, automation tools, and full-stack applications. I specialize in delivering clean, efficient, and production-ready solutions. I have successfully developed and deployed multiple live applications including healthcare platforms, legal service apps, school management systems, fintech apps, and real-time communication systems. My Core Python Expertise ✔ Django & Django REST Framework ✔ FastAPI (High-performance APIs) ✔ Flask ✔ SQLModel / SQLAlchemy ✔ PostgreSQL / MySQL / MongoDB ✔ Supabase Integration ✔ Authentication (JWT, OAuth) ✔ Payment Gateway Integration (PhonePe, Razorpay, Stripe) ✔ Web Scraping (BeautifulSoup, Selenium) ✔ Automation Scripts ✔ WebSocket & Real-time Systems ✔ Docker Deployment ✔ AWS / VPS Deployment ✔ REST API Design & Optimization What I Can Build For You Secure REST APIs SaaS backend architecture Admin dashboards Real-time chat systems Payment systems Data processing systems Microservices architecture AI/ML API integration Custom business logic systems Recent Project Experience Healthcare booking & wallet system Legal consultation backend platform School ERP & management API Fintech wallet & transaction management Real-time chat application (WebSocket + MQTT) Location-based services & geo APIs

@Qloron0804

Hi, We can build the complete time-series prediction workflow in Python, covering feature engineering, model training, validation, iteration and reproducible experiments, with clean documentation and version-controlled code. We can integrate the forecasting model with your existing Node.js, React and PostgreSQL platform through secure REST or GraphQL APIs. Model outputs can be exposed through APIs, with endpoints for submitting new data and triggering controlled retraining workflows. On the frontend, we can build intuitive React components to display forecasts, trends and basic model performance metrics in real time. The architecture can remain modular so the model and web platform can evolve independently. We can support Python based ML workflows, data processing, API development, full-stack development and data visualization. We can share relevant project details upon your response. A few questions: 1. What type of time-series data and prediction target are you working with? 2. Approximately how much historical data is available? 3. Do you have a preferred TensorFlow/PyTorch model approach? 4. Should retraining be manual, scheduled or triggered by new data? 5. Which cloud environment is your current platform using? Let’s get connected to review the data and define the first milestone. Note: The final cost may vary based on the confirmed scope. Best Regards, Agnidipa Team Qloron

@VividusDesigns

Hi, I have 8+ years of AI/ML and full-stack development experience across Python (PyTorch, TensorFlow, pandas), Node.js, React and PostgreSQL. For your platform I will build the time-series pipeline end to end: data audit, feature engineering (lags, rolling windows, seasonality), baseline models followed by an LSTM/Temporal CNN or gradient-boosting model, walk-forward validation and experiment tracking, so the results stay reproducible and easy to improve. I will then expose forecasts through secure REST (or GraphQL) endpoints in your Node.js backend, add a retraining endpoint for new data, and build React components that show forecasts, confidence bands and performance metrics in real time. A 10–15 hour weekly cadence with agreed milestones works well for me. I'll also provide a version-controlled repo, documented notebooks and code, deployment steps, and ongoing support. I can make it with a big smile, promise. Please DM me for further discussion. Thanks, Vivek Tailor