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Dedicated ML engineers

Hire machine learning engineers who start with your data, not with a model.

Need forecasting, scoring, classification or recommendations built on the data you already collect? DayDecoder hires machine learning engineers from Ahmedabad, India who write the Python pipelines, check whether the data can support a model at all, and put the result inside your product. Overlap hours for the US, UK, Europe and Australia.

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What our machine learning engineers work on.

Machine learning here means predictive analytics built on the data you already collect: forecasting, scoring and recommendations, as listed in our AI service.

The public portfolio shows the data side of this work more than finished models, and we will be clear about that. The document intelligence platform uses Python with OCR for extraction and classification with review queues. Backtesting report automation processes strategy history with Python and Pandas, calculates metrics and compares runs. Factory production monitoring and real-time analytics dashboards collect machine, shift and financial data that forecasting and anomaly detection depend on. Our article on how to train and prepare your own AI model explains when training is worth it and when it is not.

An ML hire at DayDecoder spends the first weeks on data: what exists, how clean it is, whether labels are reliable, and whether a simple rule or a SQL report already answers the question. Only then do we choose a model, measure it against a baseline and put it into the product with monitoring. If the data cannot support a model yet, we will say so and help you collect the right data.

  • Forecasting

    Demand, stock and usage forecasts built on your history, compared against a simple baseline so the gain is visible.

  • Scoring and classification

    Lead, risk or document scoring with thresholds you control and a review queue for borderline cases.

  • Data pipelines

    Python and SQL jobs that clean, join and prepare data on a schedule, with checks that catch bad inputs.

  • Models in the product

    Predictions served to your Laravel or Node.js app, logged and monitored for drift.

Skills and tool stack

The ML stack we hire for.

Python for data and models, SQL databases we already run, and the product frameworks that use the predictions.

Expect Python with Pandas for data work, scikit-learn for classical models, notebooks for exploration and scheduled scripts for production, PostgreSQL or MySQL as the source of truth, and hosted LLMs when text understanding is the better tool. Fine-tuning is considered only when retrieval and prompting have failed and you have rights to the data.

Data

  • Python
  • Pandas
  • SQL (PostgreSQL / MySQL)
  • Scheduled pipelines

Models

  • scikit-learn
  • Baselines first
  • Hosted LLMs for text
  • Fine-tuning when justified

Delivery

  • Prediction APIs
  • Laravel or Node.js integration
  • Batch scoring jobs

Monitoring

  • Accuracy against baseline
  • Data drift checks
  • Review queues

Engagement models

A dedicated ML engineer, part-time, or hourly.

Models need data work before and monitoring after, so short engagements rarely finish the job.

Dedicated full-time

Owns data pipelines, models and monitoring for a product where predictions matter every week.

Part-time dedicated

Reserved days each week for one model at a time, alongside your data or backend team.

Hourly ML help

A data readiness review, a second opinion on a model, or a fix to a pipeline that stopped. Time-logged.

Hiring process

How an ML hire starts.

Data first, baseline second, model third.

  1. 1

    Intro call

    Thirty minutes with the founder about the decision you want to improve and the data you have.

  2. 2

    Written proposal

    Dedicated, part-time or hourly, starting with a data readiness review rather than a promised model.

  3. 3

    Data access under NDA

    Read-only access to a copy or extract of the data, kept in your environment where possible.

  4. 4

    Baseline and first model

    A simple baseline, then a first model measured against it on held-out data.

  5. 5

    Weekly review

    Results against the baseline, what changed in the data and the next step, in your overlap hours.

ML work across US, UK, EU and Australian hours.

We work from Ahmedabad, India. Training runs and batch scoring can run in Indian hours, with results ready for your morning. Reviews happen in overlap: afternoons for the UK and Europe, early mornings or late evenings for the US, and mornings for Australia.

Data stays in your cloud and region. If regulations limit where data can go, we design the pipeline around those limits from the start.

  • United States

    Morning updates waiting in your inbox when the US workday starts.

  • United Kingdom

    Same-day overlap for calls, reviews, and decisions.

  • Europe

    Afternoon overlap with Central and Eastern European teams.

  • Australia

    Early-morning calls that fit the Australian workday.

Why hire machine learning engineers from DayDecoder.

Engineers who build the product and the pipeline, and who will tell you when you do not need a model.

  • Data work we already do

    Extraction, reporting and monitoring pipelines in Python are in our public projects.

  • Baselines, not hype

    Every model is compared with a simple alternative. If the gain is small, you will see it.

  • Into the product

    Predictions are wired into your app with logs and review queues, not left in a notebook.

  • You own it all

    Data, features, models and code belong to you. Your data is never used for other clients.

Relevant work

Data and AI projects closest to ML work.

Projects with Python data pipelines, extraction, classification and analytics. Client names stay confidential.

Related reading from our blog.

All articles

Related services and hires.

Services

Related hires

FAQ

Questions about hiring machine learning engineers.

Straight answers on the stack, ownership and time zones. Email us if yours is missing.

contact@daydecoder.com

Our public projects show Python data pipelines, document classification and analytics. We do not claim trained models or accuracy figures we cannot show.

Let's talk

Need a machine learning engineer on your product?

Tell us what you are building. You will get honest advice on whether you need a dedicated hire, part-time capacity, or a scoped build.

  1. 1

    We reply within 24 hours

    A real person reads your message and replies on the next business day at the latest.

  2. 2

    Free 30-minute call

    We talk through your goals, users, and budget, and answer every question.

  3. 3

    Proposal & fixed quote

    You get a written scope, timeline, and price. No obligation.

contact@daydecoder.com Ahmedabad, India

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