LLM Application Engineering
- Prompt engineering & evals
- Tool / function calling
- Streaming UX patterns
Hire AI & ML Specialists
Hire AI Engineers
AI Engineers Who Productionise GenAI & ML
Hire AI engineers who turn LLM prototypes into production systems — RAG pipelines, agent workflows, evaluation harnesses, and reliable model serving.
- Proof of Work timesheets
- AI-Augmented. Human Governed.
- Flexible contracts, transparent pricing
- 7-day risk-free trial, zero overheads
- Onboard in 24–48 hours
7-day risk-free trial
No commitment, replace anytime
- Top 1% engineers
- Onboard in 24\u201348 hours
- NDA + IP protection
- Flexible contracts, transparent pricing
Trusted Innovation Partner for Industry Leaders
Our Expertise
What Expertise Do Our AI & Data Offer?
Our dedicated hire ai engineers build production-ready systems using modern frameworks and tooling.
RAG & Knowledge Systems
- Chunking & retrieval pipelines
- Vector DBs (Pinecone, Weaviate, pgvector)
- Hybrid search + reranking
Agent Workflows
- Multi-agent orchestration
- Tool use, memory, planning
- Guardrails & safety nets
Custom Model Training
- Fine-tuning & LoRA
- Open-source LLM hosting
- Distillation & quantisation
Evaluation & Monitoring
- Automated regression evals
- Hallucination & drift monitoring
- Online experimentation
AI Infrastructure
- GPU autoscaling
- Inference cost optimisation
- On-prem / hybrid deployments
Our Capabilities
Explore the Skills That Drive Your Success
A modern tech stack for every need from PHP & Laravel monoliths to React Native apps, AI pipelines and enterprise cloud platforms.
30+
Technologies Mastered
24+
Years in Business
20+
Industries Served
1000+
Developers on Bench
PHP
DotNet
Node.js
Python
Laravel
Azure
Google Cloud
DevOps
AWS
Wordpress
Drupal
SharePoint
ServiceNow
Salesforce
HubSpot
Zoho
Shopify
Adobe Commerce
React
Next.js
Unity
Unreal
Android
React Native
Ios
Swift
Xamarin
Flutter
Don't see your stack? We learn fast and we have the team to do it.
Talk to a Tech Lead
What they build
Use Cases, Systems, and the Teams That Hire Them
These are the kinds of products our engineers ship most often.
GenAI Copilots
In-product copilots that boost user productivity with safety guardrails.
Features
- Streaming chat UX
- Tool use
- Auditable answers
Client Types
- B2B SaaS
- Productivity
- Internal tools
RAG Search & Q&A
Knowledge-base assistants grounded in your private data.
Features
- Vector DB
- Hybrid retrieval
- Source citations
Client Types
- Support
- Legal
- Healthcare
Agent Automation
Multi-step agents that handle workflows end-to-end with guardrails.
Features
- Tool use
- Memory
- Human-in-the-loop
Client Types
- Operations
- Sales
- Customer success
Custom Models
Fine-tuned or trained models hosted in your environment.
Features
- Domain fine-tuning
- Self-hosted serving
- Privacy-first
Client Types
- Regulated industries
- Enterprises
- Defence
Take GenAI From Prototype to Production
90% of AI prototypes never ship. Our AI engineers harden yours with evals, observability and reliability disciplines.
Get Talent700+
Full-time Staff projects executed successfully
20+
Years of experience in this field
4500+
Total satisfied customers
Hiring Models
How you want work to move
Added hands, owned delivery, or a dedicated engineering hub. Each model removes friction and keeps accountability clear.
Team Augmentation
Staff Augmentation / Team Extension
Expand your team. Maintain control.
Add engineering capacity without changing how you deliver.
- Individual engineers or groups (1\u20133)
- Integrate into your existing team
- You manage priorities, we handle employment
- Billing:
- Time & Material, Retainer
- Best for:
- Specific skill gaps, capacity crunches
Dedicated Team
Dedicated Teams / Delivery Pods
Cross-Functional Teams That Own Delivery
Dedicated teams accountable for predictable sprint outcomes.
- Dedicated squad (4\u201310 people)
- Tech Lead + Engineers + QA
- Shared accountability for predictable sprints
- Billing:
- Milestone-based, T&M, Fixed-Cost
- Best for:
- Products needing speed and cross-team coordination
Full-Cycle Outsourcing
Development Centers
Your Dedicated Engineering Hub
Build your secure, scalable engineering hub — operated by us, owned by you.
- Long-term, scaled teams (10\u2013100+)
- Your branding, culture, processes
- Full infrastructure, HR, security & compliance
- Billing:
- Long-term retainer, BOT (Build\u2013Operate\u2013Transfer)
- Best for:
- Enterprises needing sustained large-scale capacity
How to evaluate
What to Look for When Hiring Hire AI Engineers and How to Test for It
Hire AI Engineers should be evaluated on how well they ship AI systems in the real world.
01
01
Evaluate the Profile, Not Just the Resume
Look at how they've built and shipped real AI systems in production environments.
- Experience building production AI systems
- Knowledge of Hire AI Engineers frameworks and tooling
- Understanding of performance and scalability
- Experience with secure, observable systems
What good looks like
Built and operated several AI systems end-to-end with measurable impact.
Red flag
Pure theoretical knowledge with no production examples to point to.
02
02
Technical Interview — What to Actually Test
Assess how they approach real challenges, not isolated coding puzzles.
- Ask them to design a AI feature component
- Evaluate trade-offs and architecture decisions
- Test integration handling and error recovery
- Assess code structure and maintainability
What good looks like
Walks through trade-offs clearly and proposes pragmatic solutions.
Red flag
Jumps to code immediately without clarifying requirements.
03
03
The Questions Worth Asking in Every Interview
These reveal how developers actually work — same questions, compare answers.
- "How do you handle ai systems issues in production?"
- "How do you structure ai feature code at scale?"
- "How do you ensure reliability and observability?"
- "What Hire AI Engineers projects have you shipped recently?"
What good looks like
Concrete answers grounded in projects they personally owned.
Red flag
Generic answers that could apply to any role.
04
04
Vendor Evaluation — What to Demand in Writing
These ensure clear expectations and reduce risk before development begins.
- Code quality and review standards defined
- Definition-of-done and acceptance criteria agreed
- Integration responsibilities clarified
- Security, NDA and IP protection included
What good looks like
Vendor proactively shares engineering standards and security posture.
Red flag
Vague delivery commitments and no written engineering standards.
Comparative Analysis
How Hire AI Engineers differ from adjacent roles, and when to choose them.
| Factor | AI Engineers | ML Researchers | Data Scientists |
|---|---|---|---|
Ship Your GenAI Feature in 6 Weeks
A senior AI engineer + a focused use-case = production-ready GenAI in weeks, not quarters.
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Client Feedback
What Our Clients Have to Say About Us
“Team In India helped us rebuild our business website with a cleaner design, faster loading speed, and better enquiry flow. The new website feels professional, works smoothly on mobile, and has already improved the quality of leads we receive.”
James Carter – Marketing Director
“Our old website was slow, outdated, and difficult to manage. Team In India improved the design, performance, and overall user experience. Their development process was clear, and the final website is much easier for our team to update and maintain.”
Daniel Müller — TechHaus Consulting, Germany
“We worked with Team In India for ecommerce web development, and the experience was smooth from planning to launch. They built a mobile-friendly online store with simple navigation, secure checkout, and better product management for our growing catalogue.”
Sophia Martinez — UrbanCart Retail, Australia
FAQ
Frequently Asked Questions
Do you build with OpenAI, Anthropic, or open models?
All three — selection depends on quality, cost, latency and data-residency requirements. We benchmark against your real workload.
How do you handle hallucinations?
Retrieval grounding, structured outputs, post-checks, and automated evals. We treat reliability as an engineering problem, not a "prompt" problem.
Can you self-host LLMs in our infra?
Yes — we deploy open-source models on your AWS / GCP / Azure or on-prem GPU clusters with TGI, vLLM or Triton.
Do you handle PII and compliance?
Yes — we scrub PII, support BYOK, and align with HIPAA / SOC2 / GDPR controls when required.
How do you measure AI quality?
Golden datasets, automated evals (LLM-as-judge + traditional metrics), human review pipelines, and online A/Bs.