// hire - ai/ml
Hire AI/ML developers who ship production models.
Not a proof-of-concept in a notebook — machine learning engineers who join your team, work against your data, and take a model from prototype to something that runs in production.
- Machine learning engineers (Python, PyTorch, TensorFlow)
- LLM & generative AI engineers
- MLOps & model deployment specialists
- Computer vision engineers
- NLP engineers
- Data scientists for model development
// build your team
Tell us what you're building
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definition
What does it mean to hire a dedicated AI/ML developer?
A dedicated AI/ML developer is an engineer who joins your team on a defined engagement, works against your own data and infrastructure, and owns model outcomes the way an in-house hire would, instead of handing over a one-off notebook and disappearing.
key takeaways
Syndell's AI/ML engineers work in Python, PyTorch, and TensorFlow, with LangChain and vector databases for LLM and retrieval-augmented generation projects.
Model work spans deep learning, computer vision, NLP, and generative AI — deployed on AWS SageMaker, Google Vertex AI, or Azure ML depending on where your infrastructure already lives.
Engagements are drawn from a bench built across 1,500+ delivered projects, so a shortlist is usually ready within days, not weeks of sourcing.
Syndell's engineering studio is based in Ahmedabad, India, serving businesses across the US, UK, Australia, Canada, and UAE, with a guaranteed daily overlap window against your working hours.
// why not just hire a freelancer
AI/ML work fails quietly more often than it fails loudly: a model looks fine in a notebook and then drifts within a quarter because nobody set up monitoring, or a freelancer ships a fine-tuned model and vanishes before anyone has documented how to retrain it. An embedded engineer avoids both failure modes because they are accountable to your roadmap, not a single deliverable — the monitoring, the retraining plan, and the handoff documentation are part of the job, not an optional extra billed later.
capabilities - seven
What your AI/ML hire can own.
From use-case scoping through production monitoring — pick the slice you need, or the whole pipeline.
- 01Custom AI/ML model development
Models built around your data and your use case, not a generic pretrained wrapper with your logo on it.
- 02Model training & evaluation
Training pipelines with accuracy benchmarks agreed up front, so you know what "done" looks like before work starts.
- 03Deep learning & neural networks
Computer vision, NLP, and multi-modal model work for the cases where a simpler statistical model falls short.
- 04Generative AI & LLM integration
Prompt engineering, fine-tuning, and retrieval-augmented pipelines wired into the product you already run.
- 05MLOps & deployment
Models shipped with monitoring, versioning, and a retraining cadence, instead of left to drift silently in production.
- 06AI/ML use-case scoping
A feasibility pass before any code gets written, so you know what is realistic before you commit budget.
- 07Support & maintenance
Ongoing monitoring and iteration once a model is live, priced into the engagement instead of a surprise invoice later.
how it works
Four steps to an embedded AI/ML engineer.
- 01
Scope the role
Model type, data maturity, stack, and the outcome the hire owns. One call is usually enough to size it.
- 02
Meet vetted candidates
Pre-screened AI/ML engineers from our bench — you interview them exactly like your own hires.
- 03
Trial the fit
A working period where you evaluate real output on a real ticket before committing to the full engagement.
- 04
Confirm and embed
The engineer joins your backlog, your standups, and your definition of done, with a guaranteed daily overlap window.
frameworks & platforms our AI/ML engineers work in
- Python
- PyTorch
- TensorFlow
- scikit-learn
- LangChain
- AWS SageMaker
- Google Vertex AI
- Azure ML
-- the bench you're hiring from --
years of experience
projects delivered
happy clients
countries served
client recommendation
engagement
Which engagement model fits you?
| Dedicated engineer | Hourly engagement | Fixed-scope build | |
|---|---|---|---|
| Best for | Ongoing model work across one or more projects | A defined block of model or MLOps work | A single model or pipeline with a clear finish line |
| You get | A named AI/ML engineer embedded full-time in your team | Flexible hours against a scoped deliverable | A scoped deliverable with an agreed timeline |
| Timeline | Monthly, renews | Weeks to a few months | Project-length |
No pricing tables here on purpose — seniority, stack, and scope drive cost, and a real number after one call beats a misleading one now. Tell us what you're building.
frequently - asked
Five questions,
straight answers.
01What does it cost to hire a dedicated AI/ML developer?
It depends on seniority, engagement model, and whether the work is model development, MLOps, or both. We don't publish rate cards because scope drives cost too much for a number to mean anything — book a call and you'll get a concrete estimate against your actual use case.
02Will my AI/ML engineer overlap with my working hours?
Yes. Every embedded engineer commits to a guaranteed live overlap window with your team every working day, on top of async standups and ticket updates, so you are never waiting a full day for a reply.
03What AI/ML frameworks and tools do your engineers work in?
Python, PyTorch, and TensorFlow for model development; LangChain and vector databases for LLM and RAG work; AWS SageMaker, Google Vertex AI, and Azure ML for training and deployment infrastructure.
04Can your team take over a stalled AI/ML project?
Yes. A common starting point is a short assessment of the existing model, data pipeline, and codebase, so you know exactly what is salvageable before we commit to a rebuild versus a continuation.
05How is this different from hiring a freelance AI/ML developer?
A freelancer is one person and one point of failure. With Syndell you get a vetted engineer backed by a bench of AI/ML specialists, so coverage doesn't disappear if one person is on leave or the project scope grows mid-engagement.
build the team
Explore other roles you can hire.
- All developer teams
every role Syndell staffs, from AI/ML to Shopify
- Hire data scientists
predictive models and analysis, paired with your AI/ML hire
- Hire data engineers
the pipelines that feed the models your AI/ML hire builds
- Hire full-stack developers
the application layer around a shipped model
- Hire an AI marketing specialist
AI-driven campaigns, once your models are in production
- AI consulting services
the practice this hire draws on for use-case scoping and delivery
Comparing agencies before you commit? Browse the work our teams have shipped or read our full services overview to see how AI/ML work fits alongside the rest of the practice.
