Machine learning, built to last in production
Hire a freelance machine learning developer who delivers production ML
Develop intelligent machine learning and AI systems that turn your data into predictive insights, automate processes, and enable smarter business decisions for scalable growth. Data to deployment, one engineer, direct access.
Available now, projects start within 48 to 72 hours, NDA before any data moves
Plain answer
What is a freelance machine learning developer?
A freelance machine learning developer is an independent engineer who designs, trains, evaluates, and deploys custom ML models for businesses on a contract or project basis. Unlike a full-time hire, a freelance ML developer starts in days, not months, brings senior-level expertise without salary overhead, and works directly on your problem with no intermediary. Deliverables include trained models, inference APIs, data pipelines, and MLOps infrastructure ready for production.
A freelance ML engineer handles the full technical scope: dataset preparation, architecture selection, training experiments, hyperparameter optimisation, evaluation, and deployment. The distinction from a data scientist is important: a data scientist analyses data and builds experiments, while an ML developer engineers the production system that runs reliably at scale. Freelance engagements run from 2-week proof-of-concepts to 6-month dedicated builds, and you own all code, models, and weights outright.
Run the numbers first
What silent drift could be costing you
This is a planning tool, not a published benchmark. Set the decay rate to whatever you believe is realistic for your model, and see what an unmonitored model degrading quietly is worth in decisions gone slightly wrong.
Illustrative only, driven entirely by the assumptions you set above, not a published industry figure. Real decay rates vary enormously by domain. The point of this tool is to make the shape of the risk visible, not to predict your exact number. A model audit gives you a real one.
Job title decoder
ML developer vs ML engineer vs data scientist: which do you need?
One of the most common pre-hire questions. Most hiring managers searching "hire ML developers" or "hire machine learning developers" need the ML developer or ML engineer profile: someone who ships working systems, not just notebooks. That is what I do.
| Role | Primary focus | Output | When to hire |
|---|---|---|---|
| ML developer | Building ML-powered applications and APIs | Production ML app, inference API | You need ML integrated into a product |
| ML engineer | Model architecture, training pipelines, MLOps | Trained model, pipeline, monitoring | You need robust, scalable ML infrastructure |
| Data scientist | Exploration, analysis, statistical modelling | Notebooks, reports, insights | You need to understand data before building |
| Freelance ML developer (Shreyans) | Full scope: data to production | Model plus API plus documentation plus monitoring | You need end-to-end from one engineer |
Freelance, dedicated, or in-house: which model fits?
| Factor | Freelance (project-based) | Dedicated engagement | In-house hire |
|---|---|---|---|
| Cost | $ fixed per project | $$ monthly retainer, no benefits overhead | $$$$ salary plus benefits plus equity |
| Start time | 48 to 72 hours | 1 week | 3 to 6 months |
| Commitment | Scoped deliverable | Ongoing, scalable hours | Permanent headcount |
| Access to engineer | Direct, always | Direct, always | Direct, always |
| Best for | Defined scope, 2 to 12 weeks | Growing ML roadmap, 3 to 12 months | Core product IP, long-term |
| Risk | Low, milestone-based | Low, monthly cancel option | High, hiring risk plus notice period |
Need a dedicated engineer embedded in your team
I offer dedicated monthly engagements: a set number of hours per week, direct Slack and meeting access, and sprint-based delivery. Dedicated clients get priority availability and continuous model improvement cycles.
What I build
Machine learning development services
End-to-end ML solutions that turn data into intelligent, self-learning systems, from predictive analytics to automation. Violet tags target a specific platform. Amber is the ongoing operations layer.
Custom ML model development
Models matched precisely to your data and business objective: supervised, semi-supervised, or unsupervised, on tabular, text, image, or time-series data. Output: trained model plus inference API plus evaluation report.
- Classification and multi-label prediction models
- Regression and time-series forecasting
- Ranking and scoring models for recommendations and lead scoring
Predictive analytics and forecasting
Convert historical business data into forward-looking predictions: demand forecasting, churn prediction, revenue forecasting, risk scoring, with explainable models stakeholders can trust and act on.
- Sales and demand forecasting, MAPE benchmarks provided
- Customer churn prediction with SHAP-based explanations
- Risk and credit scoring models
Deep learning and neural networks
For tasks where classical ML reaches its ceiling: image classification, speech recognition, multi-modal inputs, complex pattern detection. I select the right architecture and train it within your compute budget.
- Image and video classification: CNNs, EfficientNet, YOLO
- Sequence modelling: LSTM, GRU, Transformer
- Multi-modal models combining text, image, and structured data
ML for web applications
Integrate machine learning directly into your web application: real-time prediction APIs, recommendation engines for web platforms, and intelligent search, deployable to AWS, GCP, or Azure.
- Real-time prediction APIs for web front-ends
- ML-powered search and ranking for web platforms
- A/B testing infrastructure for model comparison in production
ML for mobile applications
On-device and cloud-backed ML for iOS and Android: lightweight models optimised for mobile inference, plus cloud APIs mobile apps call for heavier inference tasks.
- TensorFlow Lite models for on-device inference
- Core ML integration for iOS
- Cloud ML API backends for Android and cross-platform apps
Recommendation systems
Personalisation engines for ecommerce, content platforms, and SaaS products: collaborative filtering, content-based, and hybrid approaches, built and A/B tested against revenue metrics.
- Collaborative filtering and matrix factorisation
- Content-based and hybrid recommendation models
- Real-time recommendation APIs with sub-100ms latency
Anomaly and fraud detection
Identify unusual patterns in transactions, logs, sensor data, or user behaviour with real-time pipelines that flag issues without flooding teams with false positives.
- Unsupervised anomaly detection: Isolation Forest, Autoencoders
- Real-time fraud scoring via API
- Threshold calibration to control the precision vs recall trade-off
Natural language processing
Text classification, sentiment analysis, named entity recognition, document intelligence, and LLM-powered features. See the dedicated NLP page for full scope.
- Text classification and intent detection
- Sentiment and entity analysis
- LLM-powered features: summarisation, Q&A, generation
MLOps, monitoring, and retraining
A model that is not monitored will degrade. I set up model versioning, drift detection, performance dashboards, and automated retraining pipelines that keep accuracy stable as real-world data shifts.
- MLflow model registry and experiment tracking
- Drift detection with automated alerts
- CI/CD pipeline for retraining on new data
The story behind the demo above
Why monitored models outperform models that are deployed and forgotten
A trained model is a snapshot of the world at training time. As real-world data shifts, whether from new fraud patterns, seasonal demand, or changing customer behaviour, the model's accuracy quietly erodes. Nothing crashes and no error gets thrown. The predictions just get worse, one query at a time, until someone notices the outcomes have drifted from what the model promised. Monitoring and scheduled retraining catch that erosion before it compounds.
When the cost of a stale model is immediate
- Fraud and anomaly detection, where attack patterns evolve week to week
- Real-time bidding or pricing, where a wrong call is a lost transaction
- Any model whose training data has a short shelf life
When drift is slower and predictable
- Demand forecasting, where seasonality is the main driver of change
- Churn prediction, where customer behaviour shifts gradually
- Models with a stable, well-understood feature distribution
Both paths use the same underlying infrastructure: a registry, a drift metric, and a retraining trigger. The difference is how sensitive the trigger is, and that is exactly the kind of decision a discovery call resolves before any pipeline gets built.
Expectations, in writing
What results to expect: ML project benchmarks
Concrete expectations based on delivered projects. Every engagement includes a written technical spec with target metrics before work begins. Benchmarks assume reasonably clean, domain-specific training data, audited in the discovery phase. If data is insufficient, you will hear that before any billing begins.
| Project type | Typical accuracy / performance | Timeline to production |
|---|---|---|
| Text classification (BERT fine-tune) | 88 to 94% F1 on held-out test set | 3 to 5 weeks from labelled data |
| Tabular prediction (XGBoost / LightGBM) | AUC-ROC 0.85 to 0.94 depending on data quality | 2 to 4 weeks from clean dataset |
| Demand forecasting (LSTM / Prophet) | MAPE 8 to 15% on a 30-day horizon | 3 to 6 weeks from historical data |
| Recommendation engine | 10 to 25% CTR lift vs baseline in an A/B test | 4 to 8 weeks including A/B setup |
| Fraud / anomaly detection | Precision 90%+ at agreed recall threshold | 3 to 5 weeks from labelled transaction data |
| LLM fine-tune (QLoRA) | Task-specific accuracy 15 to 30% over base model | 2 to 4 weeks from instruction dataset |
Where it runs
Industries and use cases
Domain-adapted training experience across these industries means faster ramp-up and higher baseline accuracy on your data.
Ecommerce and retail
Demand forecasting, product recommendation engines, dynamic pricing models, returns prediction, visual search.
Healthcare
Patient readmission risk, medical image classification, clinical note extraction, ICD code prediction.
Fintech
Fraud detection, credit risk scoring, transaction anomaly detection, document parsing for loan processing.
SaaS and B2B
Churn prediction from usage signals, lead scoring, support ticket priority models, NPS text classification.
Manufacturing
Predictive maintenance from sensor data, defect detection in production imagery, yield optimisation models.
Real estate
Property valuation models, demand forecasting, lead intent scoring from behavioural data.
HR and recruiting
Resume-to-job matching, candidate success prediction, workforce demand forecasting.
Your industry not listed?
The training approach transfers. The data source is what changes.
Talk it throughPricing
Hiring models and pricing
Transparent pricing. Fixed-price projects where possible. All engagements start with a free 30-minute discovery call and a written spec before any billing.
Proof of concept
Baseline model plus evaluation report. Answers the question: will this work on our data?
Project-based
Scoped deliverable: model plus API plus documentation plus 30 days of support, milestone-based billing.
Dedicated ML developer
Set weekly hours, sprint-based delivery, direct access, priority availability.
Hourly consulting
Architecture reviews, model audits, technical advisory. Useful before committing to a build.
Why a freelance ML developer from India
Senior ML expertise at 40 to 60% lower cost than US and UK rates, with direct communication, full English proficiency, and overlap with European and US business hours. No agency markup.
How it gets built
Process for building machine learning solutions
A structured development process, including data analysis, model training, and performance optimisation, built to create scalable, production-ready AI systems that generate measurable business impact.
PHASE 01days 1 to 2Discovery
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PHASE 02the decisive oneData audit and preparation
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PHASE 0348 hours inArchitecture selection and baseline
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PHASE 04iterated to targetTraining, optimisation, and evaluation
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PHASE 05handoffDeployment and integration
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PHASE 0630 days plusMonitoring, drift detection, and support
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Tooling
Technologies used in machine learning development
Advanced ML frameworks, MLOps tools, and cloud platforms, selected per project to build high-performance models that deliver accurate predictions and real business value.
Features
Why work with Shreyans Padmani
Building machine learning solutions that turn data into useful insights and support smarter business decisions.
Custom ML models
Every dataset is different, so I build machine learning models tailored to your data and business goals, ensuring reliable and meaningful results.
Predictive analytics
Systems that analyze patterns in your data to predict trends, automate decisions, and help improve planning and efficiency.
Seamless integration
Machine learning solutions are designed to fit into your existing software and workflows, making adoption simple and practical.
Delivered work
Deep expertise in advanced AI technologies
Three delivered projects with the numbers that actually shipped.
AI resume screening: custom NER plus ranking model on 10,000+ resumes
A hiring platform needed automated candidate shortlisting. I trained a custom NER model for skills, education, and experience extraction, and a ranking model for job-fit scoring. Deployed on AWS, processing 10,000+ PDFs per month.
AI video intelligence: Whisper plus extractive NLP summarisation pipeline
A training and enablement platform needed automatic meeting and video summaries. I built an end-to-end pipeline: Whisper for transcription, a custom extractive NLP model trained on meeting corpora for summarisation.
AI customer feedback classifier: 14-class BERT fine-tune on 8,000 labels
Manual survey analysis took 3 days per reporting cycle. I fine-tuned a BERT model on 8,000 labelled open-text feedback records across 14 business categories.
About
Freelance ML engineer
I am an independent machine learning engineer and AI developer with 5+ years building and shipping production ML systems for companies in SaaS, healthcare, fintech, ecommerce, and manufacturing. I have worked across the full ML lifecycle: from raw data through to monitored, continuously improving production models.
Every client I work with gets direct access to me: the engineer who scopes, builds, and deploys their model. No account managers, no outsourcing, no junior developers doing the actual work. This is the core advantage of hiring a freelance ML developer over an agency.
FAQ
