Hire AI / ML Developers for the USA
Hire AI and ML developers for your US team
AI projects stall when no one owns the path from prototype to production, and an in-house search for data, model, and engineering skills can take months. We are a software company based in India, and we provide dedicated AI and ML developers who work remotely inside your US team's tools, sprints, and repo, with calls and demos scheduled around US time zones.
- LLM, machine learning, and retrieval engineers, for US teams
- Deployment and monitoring covered
- Calls and demos scheduled around ET, CT, MT, and PT
Overview
AI and ML depth for US teams, added to your team on your terms
Hiring AI and ML developers for your US team should add capacity to your roadmap without handing your data and models to a vendor you cannot see into. You keep direction over priorities; we bring the engineering experience and the hands to build.
What does it mean to hire AI and ML developers for a US team?
It means adding engineers who build, integrate, and run machine learning and generative AI systems to your US team as a dedicated team, embedded staff, a scoped project, or a support retainer. They work remotely from our team in India, inside your tools and processes, and you keep day-to-day direction of the work.
We are open about where we are: Ethnic Infotech is a software company based in Surat, India, and our developers work remotely for US clients. The trade-off is simple. You get flexible AI and ML capacity without a US hiring cycle, and we agree working-hours overlap with you up front.
This page is for US teams. For our full offer, see Hire AI / ML Developers, and for our wider US service line, see Software Development Company USA.
Why US Teams Hire Us
Common reasons US teams bring in dedicated AI and ML developers
A hiring decision like this is usually triggered by a specific, recognizable gap rather than a general staffing preference.
Roadmap Outpacing Capacity
The AI backlog keeps growing faster than the current team can realistically ship it.
A US Hiring Search Takes Months
A team that covers data, models, and production engineering can take months to hire, and a full-time hire is a long commitment for work that may be project-shaped.
A Prototype That Never Ships
A demo works in a notebook, but no one owns the path to a system people actually use.
Data That Needs Work First
Models are only as useful as the data and workflow around them, and that groundwork needs an owner.
Data Handling Needs Settling Early
Which model provider sees your data, and under what terms, is a decision for your team and your counsel, not an afterthought.
Regulated US Sectors Raise the Bar
Healthcare, finance, and other regulated sectors have requirements your compliance team will specify, and the build has to follow them.
A Live System Needs Monitoring
Production AI needs deployment, versioning, and monitoring to stay useful over time.
Continuity Across Releases
The same developers should still know your system months later, not restart context each time.
Ways To Hire
Engagement models for adding AI and ML capacity to a US team
Scope and team size are agreed around your actual gap, not a fixed package. No rates are shown here, because a number quoted before we understand your data and use case would be a guess. Pricing is set out in your proposal.
Dedicated Team
Developers working only on your AI and ML roadmap, for steady delivery against continuous priorities.
Staff Augmentation
One or two developers join your in-house team, follow your processes, and work alongside your own engineers.
Project-Based Engagement
A defined scope against written milestones, such as a proof of concept, a pilot, or a production rollout, then roll off or move to support.
Support & Optimization
A retainer for monitoring, tuning, and improvement after launch.
What Our Developers Do
Six areas our AI and ML developers cover for US teams
Hire for one focused gap or standing capacity across the whole stack. It is the same team either way.
Generative AI
LLM & Generative AI
Assistants, copilots, and content support built on hosted or open-source models, chosen for the experience and constraints of the use case.
- Assistants and copilots on OpenAI, Anthropic, Gemini, or open-source models
- Prompt, context, and guardrail design
- Model choice weighed on quality, latency, privacy, and cost
- Testing and evaluation of responses
Predictive
Machine Learning & Data Science
Classic machine learning still matters for prediction, classification, scoring, and data-driven business logic that goes beyond language models.
- Prediction, classification, and scoring models
- Recommendation systems and anomaly detection
- Data assessment and preparation
- Python, scikit-learn, TensorFlow, and PyTorch
See our AI and ML development services →
Grounded Answers
Retrieval, RAG & Search
Retrieval architecture supports grounded answers, knowledge access, and semantic search, so responses draw on your own content.
- RAG pipelines for grounded answers
- Embeddings and vector database options
- Semantic search workflows
- Search and knowledge assistants
Automation
Automation & Workflows
Workflow logic, retrieval, and guardrails that turn models into business-ready systems for documents, support, and internal operations.
- Workflow automation and orchestration
- Document processing and summarization
- Customer support automation
- Agent-style architectures where they fit
Connected Systems
Integrations & Data Systems
AI becomes more useful when it connects to the platforms a business already runs, through APIs and custom backends.
- CRM and ERP systems
- Internal knowledge bases and document repositories
- Support platforms and communication tools
- APIs, custom backends, and analytics or reporting tools
Need the application around the model? See Web App Development →
Operations
MLOps, Deployment & Monitoring
Production AI needs deployment, versioning, monitoring, and operational controls to stay useful over time.
- Docker and cloud platform deployment
- API deployment for models and workflows
- Versioning and observability
- Monitoring and improvement after launch
Either Approach
Developers for generative AI or classic machine learning
Hire for the skill, not a single technique. Many projects combine both, and the same team recommends an approach after a data assessment.
Language, documents, and conversation
Assistants, summarization, search, and content support built on hosted or open-source models, grounded in your own content through retrieval.
- Language or documents are the core of the task
- Answers must be grounded in your content
- Model choice depends on quality, latency, privacy, and cost
Prediction, scoring, and classification
Models built on your structured data for prediction, classification, scoring, and anomaly detection, using frameworks such as scikit-learn, TensorFlow, and PyTorch.
- You have historical data and a clear target
- Scores or classes drive a business decision
- A language model is not the right tool for the task
Not sure which fits your use case? Our AI and ML development services page walks through the use cases we support.
Working Across Time Zones
How working with a team in India works for a US team
These are the practical details US buyers ask about first. Specifics are confirmed in your proposal.
Time zones
Discovery calls, sprint demos, and support windows are scheduled around Eastern, Central, Mountain, and Pacific time.
Working hours
The exact overlap and coverage hours are agreed for each engagement. We do not make blanket promises about availability.
Communication
Slack, Zoom, and Jira, or your own tools, with developers working inside your sprint process.
Contracts and billing
NDA on request, and USD invoicing for US clients.
What a remote team does not give you is a developer in your office, or in your exact time zone all day. If same-hours availability or on-site presence is a hard requirement, say so on the first call and we will tell you plainly whether we are the right fit.
How It Works
A clear path from first call to shipping alongside you
The same four stages apply regardless of team size.
Scope
We review your use case, data environment, and the gap you are filling, then recommend a model.
Output: Recommended fitMatch
You meet the specific developers proposed and approve the fit before anything starts.
Output: Confirmed teamEmbed
Developers join your standups, sprints, and repository, with a visible shared backlog.
Output: Active sprint cycleScale
Flex the team up or down as the roadmap shifts, without losing system context.
Output: Adjusted capacityWhere Our Proof Stands
What we can and cannot show you today
AI and ML buyers rightly ask for evidence, so here is the plain position.
We do not currently publish an AI or ML case study
Our published US-region projects, such as Fusion5 and Zolamod, are web platform and storefront builds, and we do not present them as AI work. If AI or ML experience is decisive for your decision, ask for specifics on the scoping call and judge the answer before committing.
You can see everything we do publish on our case studies page.
Why Ethnic?
Developers who behave like staff, not a vendor
We connect strategy, build, integration, and support with flexible team size into one accountable engagement.
- Strategy, build, integration, and support in the same team
- AI scoped around workflow value, not the newest model for its own sake
- Open about where we are: a remote team based in India
- Calls and demos scheduled around US time zones
- Work inside your existing tools, and you keep day-to-day direction
- Honest scoping: no rates quoted before we understand your data and use case
Related Services
Connect hiring with the wider AI roadmap
Use a dedicated developer alongside a scoped project when the work calls for both.
Frequently Asked Questions
Hiring AI and ML developers for US teams: FAQs
Direct answers about location, time zones, data handling, proof, and flexibility.
Can I hire AI and ML developers from Ethnic Infotech for my US team?
Yes. We provide dedicated, embedded, and project-based AI and ML developers to US teams. They work remotely from our team in India, inside your tools and sprints, and you keep day-to-day direction of the work. A US-based hire gives you same-hours presence and local employment, while a remote team gives you flexible capacity without a hiring cycle, and we say so plainly if a US hire is the better fit.
Where are your developers located?
Ethnic Infotech is a software company based in Surat, India, and our developers work remotely for US clients. This page describes remote delivery. If you need developers physically in the US, tell us on the first call so we can say plainly whether we are the right fit.
How does time zone overlap work for US clients?
Discovery calls, sprint demos, and support windows are scheduled around Eastern, Central, Mountain, and Pacific time. The exact overlap and coverage hours are agreed for each engagement, and we do not make blanket promises about availability. See Working Across Time Zones.
Which models and technologies do your developers work with?
Our AI and ML pages list OpenAI, Anthropic, Gemini, and open-source models for generative AI; Python, scikit-learn, TensorFlow, and PyTorch for machine learning; vector databases, embeddings, and retrieval pipelines for search; and Docker, cloud platforms, and monitoring tools for deployment. We choose the combination per use case.
How is our data handled when we use a hosted model?
That depends on the provider and on your agreement with it, so we do not make claims about any provider's data handling. Our approach weighs privacy alongside response quality, latency, and cost when choosing between hosted and open-source models, and the provider's data terms are something to review with your counsel before work starts.
Do we need generative AI or classic machine learning?
It depends on the task. Language and documents point to generative AI grounded in your content, while prediction, scoring, and classification on structured data point to classic ML. Many projects use both. See Either Approach.
Can your developers work inside our existing team and tools?
Yes. With staff augmentation, developers join your standups, project tracker, and Git repository, and follow your processes rather than a separate one.
Do you have AI or ML case studies?
Not published today. Our case studies are web platform and storefront builds, and we do not present them as AI work. If AI experience is critical to your decision, ask for specifics on the scoping call and judge the answer before committing. See Where Our Proof Stands.
Do you build AI for regulated US sectors, and do you provide compliance advice?
We do not provide legal or compliance advice and we make no claims about HIPAA, PCI, state privacy laws, or other certifications. We build to the requirements your compliance team specifies, and you should work with qualified advisers on those requirements.
How do you scope, quote, and invoice?
Every engagement is scoped to your use case. A clear, itemized quote follows an initial scoping call, and we do not quote hourly rates before understanding your data and use case. For US clients we invoice in USD, and contract details, including an NDA on request, are confirmed in your proposal. Timing is confirmed on the same call.
Add Capacity This Sprint
Add experienced AI and ML developers to your US team
Tell us where you are short-handed. We will come back with a real recommendation on team shape, size, and timing across your time zone.