Shreyans Padmani is a freelance AI and ML developer with a 100% Upwork job success score, a Microsoft AI certification, and 12 published case studies covering real business outcomes. If you need a production-ready AI system built by the person who will actually build it, this is the right place.
Consulting and bug fixes on-demand. Perfect for short-term audits.
Dedicated bandwidth for ongoing model training and development.
End-to-end delivery with defined milestones and clear ROI.
Most AI portfolios show architecture diagrams. These show what actually shipped and what it changed for the business.
Manual resume review was taking hours per hiring round and missing qualified candidates. An NLP-based parsing and scoring system reduced hiring review time by 70 percent. Built using custom entity extraction and a scoring model trained on the client's own criteria, not a generic matching engine.
Long meeting and training video review consumed over 40 hours per month across the team. An AI pipeline combining speech-to-text transcription and abstractive summarisation cut review time from 45 minutes per video to under 5. No proprietary SaaS dependency, fully deployed on client infrastructure.
Open-text survey analysis was slow, inconsistent, and bottlenecked on analyst availability. NLP-powered automatic feedback classification delivered real-time categorisation across 12 sentiment dimensions with no human-in-the-loop required for standard inputs.
Production-Ready AI Built for Scale, Security & Performance
As an AI Engineer specializing in Generative AI, Machine Learning, LLM integration, and Predictive Analytics, I build secure, scalable, and high-performance AI systems designed for real-world business impact not experimental demos.
You can expect a clear and honest evaluation of your business needs and data, focused on finding AI solutions that are realistic, scalable, and built to create real impact.
Every solution is built around your business needs. Whether it’s generative AI, LLM integration, or machine learning models, the goal is to solve real problems.
I design and deploy robust, secure, and scalable AI systems built to adapt, perform, and grow seamlessly alongside your business.
From planning to deployment, I manage the full process to make sure everything works properly and continues to improve over time.
A structured, production-ready approach to designing, building, deploying, and scaling AI solutions for real-world business impact.
First, I take time to understand your business, your data, and the challenges you're facing. This helps identify where AI can truly add value instead of forcing it where it’s not needed.
Before building anything, I map out how the system should work. This includes selecting the right model, planning the data flow, and designing a structure that can grow with your business.
Once the plan is clear, I build the model based on your actual needs. It could be generative AI, predictive models, or deep learning — depending on what fits your use case best.
After development, I test the system carefully, fine-tune its performance, and deploy it in a way that runs smoothly in your real working environment.
AI models are integrated into your systems, apps, or APIs. Extensive testing ensures security, scalability, and flawless operation.
AI solutions are deployed to production with continuous monitoring, optimization, and scaling for long-term performance and impact.
Custom AI & Machine Learning Solutions for Business Growth
AI solutions designed to solve real business problems and support long-term growth. Expertise in AI agents, machine learning, generative AI, NLP, computer vision, and custom model training helps businesses automate tasks, improve efficiency, and make smarter decisions.
Build intelligent systems powered by GPT-4, Claude, Gemini, DALL-E, and open-source LLMs. Services include custom chatbot development, RAG pipelines, LLM fine-tuning, and multi-agent orchestration for real business workflows. Each system is validated against your specific data and use case before deployment.
Transform raw data into revenue-generating predictions using Python, TensorFlow, PyTorch, and scikit-learn. From churn prediction to demand forecasting, every model is validated on your actual data before it touches production. No generic benchmark scores, only real-world accuracy metrics.
Automate document processing, feedback classification, and text intelligence using BERT, SpaCy, and transformer-based models. NLP solutions typically reduce manual analysis time by 60 to 80 percent for most business use cases. Custom entity extraction, sentiment classification, and summarisation pipelines available.
Deploy visual AI for medical imaging analysis, quality control, facial recognition, and retail automation. Computer vision models are built and validated on your specific image domain, not generic pre-trained weights applied without adaptation. Supports both cloud deployment and edge device inference.
Design and deploy autonomous AI agents that plan, reason, and take action using LangChain, AutoGen, and CrewAI. AI agents handle multi-step workflows including research, data retrieval, decision-making, and API calls with minimal human intervention. Suitable for business process automation and complex orchestration tasks.
Fine-tune and train custom AI models on your proprietary data for banking, healthcare, e-commerce, and manufacturing. Includes data pipeline setup, model evaluation, versioning, and production deployment. Full training infrastructure setup from scratch or on top of your existing cloud environment.
AI Predictions 2026 highlights the move from general AI to vertical LLMs and autonomous agents that drive smarter automation and faster, industry-focused decision-making.
Discover how ML consulting helps businesses turn raw data into actionable insights using machine learning. Learn how it improves decision-making, predicts trends, and drives smarter business strategies.
Discover how AI automates insurance document processing by extracting data, reducing manual work, and improving accuracy. Learn key use cases, benefits, and future trends in AI-powered insurance automation.
Our AI developers bring domain expertise across diverse sectors to solve real business problems.
Diagnostics & patient care
Fraud detection & risk
Recommendations & search
Predictive maintenance
Adaptive learning systems
Route & fleet optimization
Property valuation & search
Inventory & demand forecasting
Discuss business challenges, data opportunities, and automation goals to identify the right AI strategy from Generative AI and LLM integration to predictive analytics and production-ready machine learning deployment.
Real feedback from clients who hired me for AI, automation, and custom ML solutions.
Working with Shreyans was a smooth experience. He understood our requirements well and built a Credit Report Automation solution that saved us a lot of manual work and made our process much faster.
– Utsav
CEO
Shreyans quickly understood our process and delivered an automation solution that made audit form filling much easier for our team. It reduced manual work, saved time, and helped us improve overall efficiency.
– Ankit
CEO
Shreyans delivered an AI chatbot that handled our most common customer queries automatically. Response times dropped significantly and our support team could focus on complex issues. The solution was well-documented and straightforward to maintain.
– Priya Nair
Product Manager
The NLP pipeline Shreyans built cut our data processing time by 70%. Outstanding technical depth and great communication throughout.
– Sara Williams
Data Lead
The computer vision solution Shreyans built reduced defect detection time by 80% on our manufacturing line. Exceptional work.
– James Okafor
Operations Manager
The generative AI system Shreyans built saved us 30 hours per week in content creation. Highly recommend for any AI project.
– Lena Müller
Marketing Director
Whether you need a freelance AI and ML developer for a defined project, a monthly retainer, or a one-off consulting call, the engagement model fits your requirements.
Best for audits, consulting & targeted bug fixes
Flexible on-demand access. Ideal for scoping calls, technical reviews, or short-term problem solving.
Best for ongoing development, model training & iteration
Dedicated bandwidth each month. Consistent development velocity with no re-onboarding overhead between cycles.
Best for defined projects with clear deliverables
End-to-end delivery against agreed milestones. Scope, timeline, and success criteria agreed before work begins.
Direct, self-contained answers to the most common questions about hiring a freelance AI and ML developer.
An AI and ML developer designs, builds, and deploys machine learning models and AI systems that automate tasks, analyse data, and generate predictions. Core work includes setting up data pipelines, selecting and training models, integrating AI outputs into existing products via APIs, and monitoring performance in production. Specialisations within AI and ML development include natural language processing, computer vision, generative AI, and autonomous AI agents. A freelance AI and ML developer handles this end-to-end rather than splitting responsibilities across a team.
Hourly rates for experienced AI and ML freelancer developers typically range from 50 to 150 USD per hour depending on specialisation and seniority. Fixed-price project rates vary by scope. A focused ML model with clean data can start from 2,000 to 5,000 USD. An end-to-end AI system with custom training, API integration, and deployment typically ranges from 10,000 to 50,000 USD depending on complexity. Monthly retainer contracts are available for ongoing development with consistent bandwidth.
An AI and ML freelancer gives you direct access to the developer building your system. There is no account manager overhead, no ambiguity about who is assigned to your project, and no agency markup, which typically adds 2 to 3 times the base developer cost. The practical tradeoff is capacity. A freelance AI and ML developer suits focused projects and ongoing contracts up to full-time bandwidth. Agencies suit large parallel workstreams that genuinely require multiple specialists working simultaneously.
A focused ML model covering a single prediction task with clean, prepared data typically takes 2 to 4 weeks from scoping to deployment. A full AI system with custom model training, API integration, and production infrastructure takes 6 to 12 weeks. The most common delay in AI projects is data quality, not development speed. If your data needs cleaning, labelling, or structuring first, budget an additional 2 to 4 weeks before model development begins.
Core stack: Python, TensorFlow, PyTorch, scikit-learn, HuggingFace Transformers, LangChain, OpenAI API, Anthropic API, FastAPI, Docker, AWS, and GCP. Frameworks are selected per project requirements rather than applied as a fixed template. LLM integration projects typically use LangChain or direct API access with custom orchestration. Computer vision projects typically use PyTorch with YOLO, ResNet, or custom architectures depending on the visual task.
Yes. Monthly retainer contracts cover ongoing model monitoring, retraining when performance drifts, and iterative feature development. Hourly contracts are available for bug fixes, audits, or one-off consultations. Every production deployment includes handoff documentation, monitoring setup, and a defined support window. For clients who want to internalise the system over time, knowledge transfer sessions are available as part of the engagement.
