Hire LLM Engineers in India — Fine-Tuned, Deployed, Production-Ready
Move past prompt engineering. CompanyBench matches you with pre-vetted LLM engineers who fine-tune, evaluate, and deploy large language models in production — not just call an API. Available on contract, contract-to-hire, or full-time.
Why an LLM Engineer
Calling an LLM API is easy. Getting consistent, evaluated, cost-controlled output from that model in a real product is not. An LLM engineer handles the parts most teams skip: fine-tuning on your data, building evaluation frameworks, managing inference cost and latency, and keeping the system reliable as usage scales.
If your team has a working prototype that hallucinates too often, costs too much per query, or can't be trusted for production traffic, this is the hire that fixes it. Need a dedicated retrieval-pipeline specialist instead? See Hire RAG Developers. Not sure which approach your project needs? Read RAG vs Fine-Tuning: How to Decide What Your LLM Project Actually Needs.
LLM Hiring Tracks, One Talent Pool
Whether you need a model fine-tuned, a retrieval pipeline integrated, or an existing prototype made production-ready — tell us your mix and we match accordingly.
Fine-Tuning & Adaptation
Adapting frontier and open-source models to your own data — not just prompting a general-purpose API.
Core Stack
Typical Use Case
Domain-specific fine-tuned models for compliant chatbots and document intelligence.
RAG & Retrieval Integration
Grounding model output in your own documents and databases with a full retrieval pipeline.
Core Stack
Typical Use Case
Document Q&A, internal knowledge search, and customer support assistants grounded in real data.
Evaluation & Production Deployment
Cost/latency optimization and production monitoring that keep a model reliable at real traffic volumes.
Core Stack
Typical Use Case
Taking a hallucination-prone, expensive-per-query prototype and making it trustworthy for production traffic.
Skills & Tools We Cover
Our LLM engineers work across the full production stack, not just fine-tuning demos.
Model Families
Fine-Tuning
Frameworks
RAG & Retrieval
Evaluation & Ops
How It Works
From requirements to a productive LLM engineer, in days.
Share Requirements
Use case, models, and current stage — prototype vs. production.
Get Matched
Pre-vetted LLM engineers matched within 24-48 hours.
Interview & Select
Interview shortlisted LLM engineers and select your fit.
Onboard & Start
Most hires are productive within a week.
Industries We've Staffed For
Fintech, Healthcare/MedTech, E-Commerce, and EdTech teams have hired LLM engineers through CompanyBench for compliant chatbots, document intelligence, and domain-specific fine-tuned models.
Fintech
Compliant chatbots and document intelligence for regulated financial workflows.
Healthcare/MedTech
HIPAA-aware, domain-specific fine-tuned models for clinical documentation.
E-Commerce
Domain-specific fine-tuned models for product search and merchandising.
EdTech
Document intelligence and fine-tuned models for adaptive learning content.
Related Hiring Resources
Compare adjacent talent pools before you hire.
Hire Generative AI Developers
Need broader GenAI app integration instead? See Hire Generative AI Developers.
Hire RAG Developers
RAG is a core LLM engineering skill — a dedicated retrieval-pipeline specialist.
Hire AI Developers
Broader AI/ML, GenAI, and MLOps hiring tracks in one talent pool.
Hire Data Engineers
For buyers whose data pipelines aren't ready for fine-tuning or retrieval yet.
How We Vet Every LLM Engineer
Every developer you meet through CompanyBench has already cleared a 5-stage vetting process built to filter for real production experience — not just interview performance.
Skills & Portfolio Screening
We review resumes, portfolios, GitHub activity, and prior project history to confirm hands-on production experience in the relevant stack before a candidate moves forward — not just listed keywords.
Role-Specific Technical Assessment
Each candidate completes a scored technical assessment built for their specific role — a coding exercise, system-design problem, or take-home task modeled on real project scenarios, not generic puzzles.
Live Technical Interview
A senior engineer in the same stack runs a live technical interview — pair-programming, architecture discussion, or a scenario walkthrough — to validate depth beyond what a written test can show.
Communication & Remote-Work Readiness
We assess English fluency, async communication habits, and remote-collaboration readiness, since every engineer works directly inside your team's workflow and tools.
Reference & Background Verification
We verify prior employment or client references and confirm identity and work history before a candidate is added to the talent pool.
Skills & Portfolio Screening
We review resumes, portfolios, GitHub activity, and prior project history to confirm hands-on production experience in the relevant stack before a candidate moves forward — not just listed keywords.
Role-Specific Technical Assessment
Each candidate completes a scored technical assessment built for their specific role — a coding exercise, system-design problem, or take-home task modeled on real project scenarios, not generic puzzles.
Live Technical Interview
A senior engineer in the same stack runs a live technical interview — pair-programming, architecture discussion, or a scenario walkthrough — to validate depth beyond what a written test can show.
Communication & Remote-Work Readiness
We assess English fluency, async communication habits, and remote-collaboration readiness, since every engineer works directly inside your team's workflow and tools.
Reference & Background Verification
We verify prior employment or client references and confirm identity and work history before a candidate is added to the talent pool.
Frequently Asked Questions
Everything you need to know about hiring LLM engineers through CompanyBench.
The terms overlap. An LLM engineer typically focuses more deeply on model fine-tuning, evaluation, and production reliability, while a generative AI developer covers broader application integration.
Yes — our vetting process screens for real fine-tuning experience, such as LoRA and QLoRA, not just prompt engineering on frontier APIs.
Pre-vetted LLM engineers are typically matched and available for interview within 24-48 hours.
Contract, contract-to-hire, and full-time — suited to bounded fine-tuning projects as well as long-term LLM product roadmaps.
Rates are significantly below US market rates for equivalent experience. Contact us for a role-specific quote based on scope and seniority.
Every LLM Engineer goes through a 5-stage process — skills and portfolio screening, a role-specific technical assessment, a live technical interview with a senior engineer, a communication and remote-readiness check, and reference verification — before joining the talent pool.