Freelance · India based · SGT overlap
A model fine-tuned or trained from scratch on your own proprietary data, versioned from the first run. Deployed on infrastructure you control, with a clear audit trail from data snapshot to model artefact.
Plain answer
A working production system, not a notebook, prototype, or deck that your team still needs to finish.
A freelance AI model training developer sets up a reproducible pipeline, recommends fine-tuning or from-scratch training based on your data, evaluates the result against your own held-out data, and ties every model version to the exact data snapshot that trained it. For a Singapore business, that also means keeping training data and weights inside infrastructure you control, and working across a practical 2.5-hour time difference.
Inside the engagement
The person who scopes the project is the same person building each layer, so fewer handoffs and clearer decisions.
Every deliverable connects to the next, so validation assumptions do not get lost between specialists.
Every training run tied to a versioned, auditable data snapshot.
A clear recommendation based on your data volume and how domain-specific the task is.
Deployed on your existing cloud, including a Singapore region, with retraining triggers defined before performance drifts.
What I build
Custom AI model training is one of six areas of AI and ML work. The person who scopes your project is the person who builds it, across all six.
Churn prediction, demand forecasting, and risk scoring, validated on your actual data using Python, TensorFlow, PyTorch, and scikit-learn.
NLPDocument processing, feedback classification, and text intelligence built with BERT, spaCy, and transformer-based models.
VisionMedical imaging analysis, quality control, and retail automation, trained on your specific image domain, cloud or edge inference.
GenAICustom chatbots, RAG pipelines, and LLM fine-tuning powered by GPT-4, Claude, Gemini, and open-source models.
AgentsAutonomous agents that plan, reason, and take action using LangChain, AutoGen, and CrewAI for multi-step workflows.
Fine-tuning or from-scratch training on your proprietary data, with versioning and production deployment.
Compare the paths
See the communication path and commitment behind three common hiring options. Singapore's National AI Strategy 2.0 aims to triple the national AI talent pool from about 4,500 to 15,000 professionals by 2027, which is part of why local AI hiring runs slow and expensive right now.
| Option | Typical cost | Time to start | Commitment |
|---|---|---|---|
| Full-time in-house | S$100,000-170,000 base (Morgan McKinley, 2026), plus CPF and benefits | Weeks to months | Ongoing headcount |
| AI development agency | Typically 2-3x a freelance developer's base cost | Days to weeks | Team capacity, higher overhead |
| Freelance AI developer | USD 50-150/hr or USD 2,000-50,000 per project | Starts within 48 hours | Scales per project |
Compliance architecture
Training data often includes personal data, so PDPA's consent and retention rules apply the same way they would to any pipeline. What is specific to model training is the audit trail: knowing exactly which data snapshot trained which model version matters for compliance, debugging, and proving what a model was and wasn't exposed to. Singapore's PDPA carries penalties of up to S$1 million or 10% of annual turnover, whichever is higher, under the 2020 amendment.
This is background information, not legal advice. Confirm your organisation's specific obligations with a qualified data protection officer or lawyer.
Transparent delivery
Interim reviews keep business assumptions, technical choices and compliance decisions visible.
A 30-minute call during the Singapore-India overlap covering your data volume, domain, and whether fine-tuning or from-scratch training actually fits the problem.
Your training data gets audited for volume and quality, and the training approach gets mapped before any compute is spent.
The model is trained and evaluated against held-out data from your own domain, with versioning in place from the first run.
The trained model goes live on your infrastructure, with retraining triggers defined before performance drifts.
Working rhythm
A Singapore morning call lands in late morning in India, while most afternoon reviews still fit the India workday.
Flexible engagement
Tooling
No mandatory framework. The choices follow your data, infrastructure and operational constraints.
Frameworks chosen for reproducibility and fit to your data volume.
Reproducible pipelines with a clear audit trail from data to model.
Production infrastructure with retraining triggers built in.
Proof over promises
Twelve case studies across machine learning, NLP, computer vision and generative AI.
Custom entity extraction and scoring trained on the client's own criteria.
“Understood our requirements well and built a solution that saved a lot of manual work.” Utsav, CEO
See the problem, approach and measured result across production AI projects.
Client testimonials
Real feedback from clients who hired an AI and ML developer for 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."
"Shreyans quickly understood our process and delivered an automation solution that made audit form filling much easier for our team. It reduced manual work and improved overall efficiency."
"Shreyans delivered an AI chatbot that handled our most common customer queries automatically. Response times dropped significantly and the solution was well documented and straightforward to maintain."
"The NLP pipeline Shreyans built cut our data processing time by 70%. Outstanding technical depth and great communication throughout."
"The computer vision solution Shreyans built reduced defect detection time by 80% on our manufacturing line. Exceptional work."
"The generative AI system Shreyans built saved us 30 hours per week in content creation. Highly recommend for any AI project."
Why work with me
Specialising in generative AI, machine learning, LLM integration, and predictive analytics, systems are built secure, scalable, and high-performance, for real-world business impact, not experimental demos.
A clear, honest evaluation of your business needs and data, focused on AI solutions that are realistic, scalable, and built to create real impact.
Every solution is built around your business needs. Whether generative AI, LLM integration, or machine learning models, the goal is to solve real problems.
Robust, secure, scalable AI systems designed and deployed to adapt, perform, and grow alongside your business.
From planning to deployment, the full process is managed to make sure everything works properly and keeps improving over time.
Industries we serve
Domain expertise across diverse sectors, built to solve real business problems, not adapted from a generic template.
About
I build intelligent AI and ML solutions that help businesses solve real problems and make smarter decisions: machine learning models, deep learning, NLP and computer vision, data analysis and insights, and AI-powered automation.
Blog
Notes on shipping production AI, written from the build, not the brochure.
The move from general AI to vertical LLMs and autonomous agents driving faster, industry-focused decisions.
02 May 2026 Data scienceHow ML consulting helps businesses turn raw data into actionable insights and sharper strategy.
01 May 2026 Machine learningHow AI extracts data and reduces manual work in insurance document processing, with key use cases.
30 Apr 2026FAQ
Clear answers on cost, compliance, location and timing.
Start with the data
Bring a sense of your data volume and whether fine-tuning or from-scratch training is even the right question yet. The first call is 30 minutes, timed inside the Singapore-India overlap.
