Serving Bangalore, India remotely

AI engineer in Bangalore

Bangalore teams usually want AI that fits inside a product or an engineering process, not a demo. I build support assistants over product documentation, lead qualifiers tied to trial sign-ups, document-processing pipelines and agent workflows, using Claude, ChatGPT and Gemini through their APIs. I work remotely from India, keep the code in your repository and test every change against a fixed set of questions before it ships.

Based in India and working remotely: no office in Bangalore, and video calls at times that suit you. Bangalore and I share UTC+5:30, so there is no time-zone difference and the full day overlaps. Software firms there often work Monday to Friday with flexible hours, while shops, clinics and schools work Monday to Saturday; calls are scheduled around the pattern your team follows.

$400K+
Monthly ad spend managed
500+
Global brands scaled
12+
Years in performance marketing
22–30x
Peak ROAS (healthcare / IVF)

AI engineering and AI-powered software for Bangalore businesses

The way I work with a Bangalore engineering team is close to how a good contractor joins a sprint. Prompts live as versioned files next to the code, the question-and-answer test set sits in the repository, and a pull request that changes a prompt, a model or a retrieval setting runs that set in your pipeline so a regression shows up before review. If an engineering group in Koramangala or Whitefield already has a design system, a logging stack and a deployment routine, I use them instead of introducing my own. A founder with a one-page brief gets the same discipline in lighter form. Either way, a person reads the failed cases rather than trusting a score from another model.

The most common request is a support or sales assistant that answers from product documentation. The weak point is rarely the model; it is stale documents. Release notes say one thing, an old help article another, and the assistant repeats whichever it finds first. I index documents with version and date, prefer the newest, link to the source page in each answer, and open a ticket with the conversation attached when the documents have no answer. Customers also paste text into the chat that tries to redirect the assistant, so I test for that, and I keep tools it can call narrow, with read access by default and approval before anything is changed.

A B2B product has a point most other cities do not raise: one customer's data must never surface in another customer's answer. I design retrieval so each tenant's documents are filtered before the model sees anything, and I write a test that asks one account for another's data and expects a refusal. I also measure what a conversation costs against the support tickets it removes, and cap monthly spending per provider. The DPDP Act 2023 applies to user data, and procurement teams at larger buyers ask how data flows, so I document it plainly. We share UTC+5:30; I have no Bangalore office, and Kannada text should be reviewed by a native speaker.

Working with a remote specialist from Bangalore

Time zone
Bangalore and I share UTC+5:30, so there is no time-zone difference and the full day overlaps. Software firms there often work Monday to Friday with flexible hours, while shops, clinics and schools work Monday to Saturday; calls are scheduled around the pattern your team follows.
How projects there are usually paid
Bangalore companies usually pay by bank transfer (NEFT or RTGS) against a GST invoice, and funded start-ups often follow a purchase-order or vendor-onboarding process with TDS deducted at source. It saves time to confirm the invoice details and payment schedule in writing at the start. Local currency: INR.
Languages
English, Kannada, Hindi, Tamil, Telugu
Rules that apply
The Digital Personal Data Protection Act, 2023 matters to any Bangalore product that collects user data, and consent screens and data-retention choices should be designed with it in mind.

What AI engineering and AI-powered software covers

Working AI feature
an assistant, qualifier or agent workflow for the agreed use case.
Knowledge base
your documents cleaned, structured and indexed for retrieval.
Test set
questions and correct answers you keep, with a record of every run.
Integrations
CRM, e-mail, WhatsApp or database connections with minimal permissions.
Controls
rate limits, spending caps, fallbacks and a human handover route.
Conversation log
a private record of what was asked and answered, without needless personal data.

AI engineer in Bangalore: questions

Yes. I keep the questions and expected answers in your repository and wire them to run on pull requests that touch prompts, models or retrieval, so regressions appear before review.

I index each document with its version and date, prefer the newest, link the source in every answer and open a ticket when no current document covers the question.

Retrieval is filtered by account before the model sees any text, and a standing test asks one account for another's information and expects a refusal.

I work inside your repository, follow your conventions and hand over documented code and prompts, so your engineers keep ownership and can extend the system without me.

No. I build applications on top of established models such as Claude, ChatGPT and Gemini through their APIs. The engineering is in the content, retrieval, integrations and testing, which is where most business results come from.

I restrict it to approved documents, require it to say when the answer is not there, hand uncertain questions to a person and test it with a fixed set of awkward questions after every change. This reduces invented answers; it cannot remove them entirely.

Yes. I can work in your repository, follow your design system and review process, and hand over documented code, so the website or feature does not depend on me later.

More for Bangalore

How the work runs

  1. 01

    Pick the use case

    A call to choose one task where AI saves real time or wins real leads, with a measure of success agreed in advance.

  2. 02

    Gather and clean content

    Collect the documents, FAQs and rules the assistant may use, remove outdated material and mark what it must never say.

  3. 03

    Build a prototype

    A working assistant or workflow on a private link, built on the model and tools that suit the task and the data rules.

  4. 04

    Create the test set

    Real, tricky and hostile questions with correct answers, run against every change and reviewed by a person.

  5. 05

    Connect and add controls

    Integrations with your tools, spending and rate limits, logging, approval steps and a clear handover to people.

  6. 06

    Launch, watch and tune

    A limited launch, daily review of real conversations for the first weeks, then fixes to content and prompts.

AI engineer for Bangalore: get a quote

Tell me the goal; you get a plain answer and a fixed quote, usually within one working day.

Fastest reply on WhatsApp — usually within a couple of hours.

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AI Engineer in Bangalore | Sunny Guha