Sarvaswa AI Labs
Hire AI Developers

Hire AI developers who have already shipped this before.

Recruiting a senior AI engineer into your own team usually takes somewhere between six and nine months once you account for sourcing, interviewing, notice periods, and the time it takes a new hire to become genuinely productive in your codebase. We offer a different route: senior AI developers, machine learning engineers, and forward deployed engineers who join your team as an embedded unit, work in your repositories, and ship production systems you own outright. You meet the engineer before you commit to anything, and most placements are working inside the team within about two weeks.

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12 specialised AI engineers · 20+ companies supported worldwide

Why this is hard right now

The people who have done this work are not on the market for long.

Hiring for artificial intelligence has an awkward property that makes it different from most other engineering hiring: the pool of people who have genuinely taken an AI system into production, rather than building an impressive prototype, is still small, and those people are rarely unemployed for any length of time. When one does become available, they usually field several offers within a fortnight, which means your process has to be both fast and confident at exactly the moment when most teams are neither.

The second difficulty is evaluation, and it is the one that quietly causes the most damage. If nobody currently inside your organisation has shipped a fine-tuned model or an agent with real tool access, then nobody inside your organisation can reliably tell whether the candidate in front of them has done so either. Interviews in that situation tend to reward fluency with terminology, and fluency is unfortunately the easiest thing in this field to acquire without the underlying experience.

The third is timing, which is simply arithmetic. A six to nine month path from opening a role to having a productive engineer is a reasonable expectation for a senior AI hire, and if your competitive window is shorter than that, the correct answer may not be a hire at all. It may be borrowing the capability now, shipping the thing that matters, and hiring afterwards from a much stronger position, because by then you will have a working system to point at and people who understand it well enough to interview honestly.

Your realistic options

Four ways to add AI engineering capacity.

Each of these is genuinely the right answer in some situation, and we have recommended all four to different teams, including the ones that meant we did not get the work. The purpose of this section is to help you recognise which situation you are actually in before you spend money finding out.

Hire in-house, permanently

Best when AI is becoming core to your product for years, not months

A permanent hire is the right decision when the work will continue indefinitely and you want the knowledge to compound inside your own walls, which is a genuinely good reason and often the correct long-term answer. The costs to plan for are the six to nine month timeline from opening the role to real productivity, the salary band that senior AI engineers currently command in a competitive market, and the risk that a wrong hire costs you both severance and the months you spent getting there.

Watch for

If nobody on your team can technically evaluate the candidate, consider borrowing that judgment before you make the offer.

Freelancers and marketplaces

Best for a bounded, well-specified piece of work with a clear finish line

When you know precisely what you need built, the specification is unlikely to change, and the work does not have to integrate deeply with the rest of your systems, a skilled freelancer is fast and economical. The difficulty appears when the requirements shift mid-project, as AI requirements usually do once the first evaluation results come back, because at that point you own the coordination problem, the architecture decisions, and whatever happens after the contract ends.

Watch for

Ask who maintains the system in month four, and make sure the answer is a person who will still be there.

A traditional consultancy

Best when you need strategy, governance, or an outside opinion for a board

Large consultancies are genuinely good at organisational change, stakeholder alignment, and producing analysis that survives scrutiny from a risk committee, and if that is what you need then they are worth their fee. The common disappointment in AI projects specifically is that the engagement produces a thorough roadmap and a set of recommendations while the actual system remains unbuilt, because the people writing the document are frequently not the people who would write the code.

Watch for

Ask directly whether the engineers in the room will be the engineers on the work, and how seniority is staffed after the pitch.

An embedded engineering team

Best when you need production AI shipped now and owned by you afterwards

This is the model we operate. Senior engineers join your team rather than working at a distance from it, attending your standups, committing to your repositories, and building inside your own cloud against your real data. Because they have taken similar systems into production before, the early architectural decisions that usually cost teams months of rework are made correctly the first time, and knowledge transfer is built into the engagement rather than promised at the end of it.

Watch for

Ask what you own at handover, and whether anything continues to depend on the vendor once they leave.

Who you can hire

The roles teams ask us for most often.

You can hire a single engineer where the work is contained, or a small pod where several disciplines have to move together, and in practice the right shape usually becomes clear during the scoping conversation rather than before it.

AI and LLM engineers

Engineers who work at the model layer rather than around it, which in practice means fine-tuning open foundation models on proprietary data, building retrieval pipelines that return the right context instead of merely relevant-looking context, designing evaluation harnesses that can prove a change was an improvement, and making the model selection decisions that determine both quality and cost.

Typical brief: build a domain model on our data and prove it beats the API we use today

Machine learning engineers

Engineers for the predictive and classification work that sits alongside language models and often matters more to the business, including forecasting, recommendation, anomaly detection, and the unglamorous but decisive work of getting a model to behave the same way in production as it did in a notebook.

Typical brief: forecast demand accurately enough that we can scale capacity ahead of it

AI agent engineers

Engineers who build systems that act rather than answer, which means tool and Model Context Protocol integration, multi-agent orchestration where one problem needs several kinds of expertise, and the safety scaffolding of scoped permissions, approval gates, and tracing that makes an agent something you can leave running.

Typical brief: automate this multi-step process without letting it touch anything irreversible

Data and platform engineers

The engineers who build the foundation everything else depends on, covering ingestion pipelines, warehouse and lakehouse modelling, vector search infrastructure, and the deployment and autoscaling work that keeps inference costs proportional to actual usage rather than to peak capacity you provisioned once and forgot.

Typical brief: our data is not ready and the AI project cannot start until it is

Forward deployed engineers

Senior engineers who embed most deeply of all, taking responsibility for a working system in production rather than for a set of tickets, and who are comfortable sitting with your operations team to understand how the work is really done before deciding what to build. This is the role to ask for when the problem is not yet specified precisely enough for anyone to quote it.

Typical brief: we know the outcome we need but not yet the system that produces it

How to evaluate

Six questions worth asking anyone, including us.

These are the questions we would ask if we were on your side of the table, and they are deliberately the ones that are uncomfortable to answer badly. Ask them of every option you are considering, and compare the answers rather than the credentials.

What have you taken into production, and what broke afterwards?

Anybody can demonstrate something impressive in a controlled setting, so the more revealing question concerns what happened once real users arrived. An engineer who has genuinely operated an AI system will describe specific failures without hesitation, because production teaches lessons that prototypes never do, and the absence of any such story usually means the absence of the experience.

How would you know this system was working?

The answer you want involves evaluation harnesses, holdout sets, and objective checks that can genuinely fail, described in enough detail that you can picture them. An answer built mainly on the phrase "it looks good" indicates that quality will be assessed by opinion, and opinions are expensive to relitigate every time somebody adjusts a prompt.

When would you tell me not to use AI here?

A person who cannot answer this is either inexperienced or selling, and neither is helpful to you. There are many problems for which a better-designed form, a fixed integration, or a conventional piece of software is the correct and considerably cheaper answer, and someone who has done this work for a while will name examples readily.

What do we own when this ends?

Get specific here, and ask about the model weights, the training scripts, the evaluation harness, the prompts, the infrastructure definitions, and the runbook, one at a time. Any component that stays with the vendor becomes a dependency you will be paying for indefinitely, and dependencies of that kind are much easier to avoid at the start than to unwind later.

Where does our data actually go?

The answer should describe your cloud, your boundaries, and your existing access controls, with a clear account of anything that crosses a perimeter and why. If your organisation carries SOC 2, HIPAA, or regional data residency obligations, this conversation belongs at the beginning of the engagement rather than in a security review three months later.

Who is actually doing the work?

Ask to meet the engineer, not the account manager, and ask whether the person in the room will be the person writing the code. It is a reasonable request and it is easy to satisfy honestly, which is precisely why the manner in which somebody handles the question tells you a great deal about how the engagement will run.

How it works with us

From first call to embedded, in about two weeks.

The process below is deliberately short at the front, because the useful information comes from working on your actual problem rather than from extended discovery, and because you should be able to change your mind cheaply if the fit turns out to be wrong.

  1. One scoping call

    A focused conversation about the problem you are trying to solve, the systems it touches, and the timeline you are working against. There is no discovery invoice attached to this, and if what you describe would be better served by a freelancer, a permanent hire, or no AI at all, we will tell you during that call rather than afterwards.

  2. We match an engineer or a pod

    We assign the engineer whose domain and engineering background genuinely fits the work, and where a problem spans several disciplines we propose a small pod instead. Because our team is twelve specialised engineers rather than a large bench, this matching is done by people who know both sides rather than by a staffing database.

  3. You meet them before committing

    You interview the engineer who would actually do the work, ask them anything, and decline if the fit is not right. We consider this step essential rather than a courtesy, because an embedded engagement depends entirely on the working relationship and it is far cheaper to discover a mismatch now than in week six.

  4. They embed and start shipping

    Your engineer joins your standups, your Slack, and your repositories, and works against your real data from the beginning rather than a sanitised copy. A working proof of concept typically lands within two to four weeks, and most engagements reach production somewhere between eight and fourteen weeks from kickoff.

  5. Knowledge transfers, then we step back

    Your team builds alongside the engineer throughout rather than receiving a handover document at the end, so involvement from our side decreases by design as your own people take the system over. At completion you hold the source code, the models, the pipelines, and the runbook, and we remain available when you want us rather than because you need us.

Commercials, honestly

What it costs, and what it is fair to compare it against.

We scope commercials after the first call, once we understand which roles are involved and for how long, because quoting before that point would require guessing at the shape of your problem and any number produced that way would be wrong in one direction or the other. What we can describe openly is the structure: engagements are scoped to outcomes and a defined duration rather than billed as an open-ended retainer, and you are told what a phase costs before it begins.

The comparison that makes sense is not our rate against a freelancer hourly figure, because those two numbers measure different things. The fair comparison is the total cost of getting a working system into production, which for a permanent hire includes recruitment fees, salary and benefits across the six to nine months before real productivity begins, and the cost of the delay itself. Our engineers are based in Surat and Bengaluru, which makes the economics of senior engineering time considerably more favourable than equivalent seniority in North America or Western Europe, while timezone coverage still works for teams across the Americas, Europe, and Asia.

For a deeper look at how an embedded engineer actually works once they join, read the Forward Deployed Engineers service page, or see the kinds of systems the team ships in AI agent development and LLM fine-tuning.

Questions worth answering

Hiring AI developers, answered.

It begins with a single scoping call covering the problem, the systems involved, and your timeline, and there is no discovery fee attached to it. We then match a senior engineer, or a small pod where the work spans several disciplines, and you interview that person before committing to anything. Once you are happy, they embed into your team, typically within about two weeks, joining your standups and working in your repositories against your real data. Most engagements produce a working proof of concept in two to four weeks and reach production between eight and fourteen weeks from kickoff.
The roles teams request most often are AI and LLM engineers who work at the model layer on fine-tuning, retrieval, and evaluation; machine learning engineers for forecasting, recommendation, and classification systems; AI agent engineers who build tool-using and multi-agent systems along with the permissions and approval gates that keep them safe; data and platform engineers for pipelines, vector search, and deployment infrastructure; and forward deployed engineers who take responsibility for an entire production outcome rather than a defined set of tickets. You can hire one engineer or a pod, and the right shape usually becomes clear during scoping.
Sometimes you should, and we will say so when that is our honest read. A permanent hire makes sense when AI is becoming core to your product for years rather than months and you want the knowledge to compound internally. The costs to weigh are the six to nine month path from opening the role to genuine productivity, the salary band senior AI engineers currently command, and the risk of a wrong hire in a field where most teams cannot yet evaluate candidates confidently. A frequent middle path is to embed a team now, ship the system that matters, and recruit afterwards from a stronger position, because you will then have a working system and colleagues who understand it well enough to interview properly.
A freelancer works well when the specification is clear, unlikely to change, and does not need deep integration with your other systems. AI requirements tend not to behave that way, because the first evaluation results usually change what should be built next. With a marketplace you retain the coordination, the architecture decisions, and the question of who maintains the system afterwards. Our engineers are accountable for a working system in production, work inside your environment rather than at a distance from it, and build knowledge transfer into the engagement so your team can operate the result without us.
We scope commercials after the first call, once we know which roles are involved and for how long, because any figure quoted before that would be a guess. Engagements are scoped to outcomes and a defined duration rather than an open-ended retainer, and you know what a phase costs before it starts. The comparison worth making is not our rate against a freelancer hourly rate but the total cost of reaching production, which for a permanent hire includes recruitment fees, salary across the months before productivity begins, and the cost of the delay. Our base in Surat and Bengaluru makes senior engineering time meaningfully more economical than the equivalent seniority in North America or Western Europe.
Remote-first, and deeply integrated rather than distant, which means your standups, your Slack channels, and your repositories rather than a weekly status call. On-site time happens where an engagement genuinely calls for it, such as an initial workshop or a period spent sitting with the operations team whose process is being automated. Our engineers are based in Surat and Bengaluru, and the timezone overlap works for teams across the Americas, Europe, and Asia.
You do, completely and from the first commit. That covers the source code, the model weights and training scripts where fine-tuning is involved, the data pipelines, the evaluation harnesses, the infrastructure definitions, and the runbook your team operates the system with. Everything runs inside your own cloud and data boundaries, with SOC 2, HIPAA, and region-specific requirements scoped at the start rather than discovered in a later security review. Nothing continues to depend on us after the engagement ends.
Most placements are embedded and working within roughly two weeks of the scoping call, which covers matching the right engineer, your interview with them, and the access and onboarding your side requires. From there a proof of concept against your real data usually lands in two to four weeks, and production follows in eight to fourteen weeks for the majority of engagements. If your situation is more urgent than that, say so on the call and we will tell you honestly whether it is achievable rather than agreeing and hoping.

Tell us what you need built, and we will tell you who should build it.

Fifteen minutes is enough to work out which of the four options genuinely fits your situation, and if that turns out to be a permanent hire or a freelancer rather than us, you will hear that on the call. If it is us, you will meet the engineer before you commit to anything.

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