Hire Generative AI Developers Who Ship LLM Systems You Can Trust in Production
A demo that answers three questions well is not a product. Hire generative AI developers from Entrans and you get engineers who build RAG pipelines, agents, and fine-tuned models. Evaluation, guardrails, and cost controls are wired in from the start. You see accuracy and cost per request, not just a working prototype. Start with one specialist or a full pod, onboarded in 48 to 72 hours.
Why Market Leaders Choose Entrans Generative AI Developers
Entrans has delivered 150+ AI projects for 200+ enterprises. Buyers who want the best generative AI developers are usually not short on model ideas. They are short on engineers who can make one hold up under real users, real data, and a real budget.
1. We Handle Hallucination Before You Ship
Every build gets an evaluation set, groundedness scoring, and citations traced back to source documents. On an AI content platform, a review gate held content accuracy at 100 percent before anything reached a learner.
2. Senior GenAI Engineers, Screened on Real Builds
Candidates are screened on retrieval design, prompt evaluation, and production deployment, not framework trivia. You interview finalists, not a pile of profiles.
3. Agentic AI Is Our Default, Not a Side Project
Entrans is built around agentic AI. Our engineers ship multi-agent workflows with tool calling, memory, and approval gates, backed by our agentic AI framework integration practice.
4. We Engineer for Token Cost, Not Just Accuracy
A feature that works but costs 40 cents a call gets killed in the next budget review. Our engineers add caching, model routing, and prompt trimming, then report cost per request beside accuracy.
5. Onboarded in 48 to 72 Hours
Profiles reach you in 24 to 48 hours and engineers start inside 48 to 72. Vendors who quote two to four weeks burn a chunk of your pilot window before anyone writes code.
Hire Generative AI Developers From Entrans Who Are Certified and Experienced
Here is the actual work every generative AI developer for hire at Entrans does in the first 90 days. Live systems, real compliance constraints.
RAG Pipeline Engineering
Build retrieval that actually retrieves. Chunking strategy, embedding choice, hybrid keyword and vector search, reranking, and citations the user can click. Plus a fallback path for when retrieval returns nothing useful.
Agent and Tool Orchestration
Design multi-step agents with tool calling, memory, retries, and human approval gates. Connect them to the ERP, CRM, and ticketing systems your team already runs, over APIs or Model Context Protocol.
Fine-Tuning and Model Adaptation
Apply LoRA, QLoRA, and instruction tuning when prompting alone misses the accuracy bar. Every run is benchmarked on your data before and after, so you know what the tuning actually bought you.
Evaluation and Guardrails
Build the eval harness: golden datasets, LLM-as-judge scoring, red-team prompt suites, PII redaction, and clean refusal behavior. This is the layer that separates a pilot from a product.
Document and Multimodal Generation
Extract structured fields from PDFs, scans, and images with OCR plus language models, and generate text, image, or video assets on brand. Structured output schemas keep the results parseable downstream.
LLMOps and Cost Control
Serve models with vLLM or managed endpoints, add semantic caching, and route cheap calls to small models. Latency, token spend, and drift all land on one dashboard.
Schedule Interviews With Generative AI Engineers and Onboard Them Within 48 to 72 Hours
Generative AI Development Technology Expertise
Foundation Models and Providers
Retrieval, Agents, and Orchestration
Training, Tuning, and Serving
Evaluation, Governance, and Observability
Our Customer Success Stories
AI Invoice and GRN Reconciliation Platform
Industry: Finance and procurement
Technical Stack: AWS Bedrock, MongoDB, PostgreSQL, Flask
The finance team matched invoices, purchase orders, and goods receipt notes by hand. Documents arrived as PDFs, scans, and structured records with no common extraction path, so payment approvals stalled. Entrans built an AI invoice and GRN reconciliation platform that extracts invoice fields with LLMs on AWS Bedrock. It matches each invoice against its PO and GRN, then flags gaps in quantity, pricing, and supplier detail. MongoDB stores the documents, PostgreSQL holds the transactional records, and a Flask service runs the workflow and reporting.
Results: 80% reduction in manual reconciliation effort and 2X faster payment processing, across PDFs, scans, and structured records.
Case Study 2: AI Generated Elearning Reel Platform
Industry: EdTech and B2B learning
Technical Stack: Next.js, React.js, Node.js, HTML, CSS, JavaScript
Static course content was losing B2B learners, and the platform had no way to publish AI-generated material as video without a studio pipeline. Standard quizzes could not test comprehension of the new format either. Entrans built an AI-to-reel generation platform with a Next.js pipeline that turns AI output into video-style reels. A custom animated player handles the transitions. A Node.js and React editing tool lets admins review every generated piece before publish. A dialog quiz module then asks learners to predict character responses.
Results: a four-component platform, 100% AI content accuracy held through admin review, and 2X higher learning comprehension on the context-based quizzes.
Designed for Enterprise Speed and Control
Hire a generative AI engineer in days, not quarters. Here is the path from first call to first commit.
1. Share Your Requirements
Tell us the use case, the data you already hold, and your stack. We map that to the skill you need, whether that is an LLM application engineer, an evaluation specialist, or an MLOps engineer. If the use case itself is still open, our generative AI consulting team scopes it first.
2. Get Curated Profiles (Within 24 to 48 Hours)
You receive a short list of pre-vetted generative AI developers whose shipped work matches your domain. No stack of resumes to sort through.
3. Evaluate and Interview
Run your own technical round, a live retrieval task, or a short paid trial. Ask each candidate about a prompt or model that failed in production and what they changed. That answer tells you most of what you need.
4. Onboard and Kickoff (Within 48 to 72 Hours)
We handle NDAs, access, and environment setup. Your engineers join your standups, your Jira, and your repositories, with genuine working-hour overlap across the US, UK, UAE, and India.
5. Continuous Support and Scaling
Add a data engineer when your pipelines grow or a DevOps engineer when inference moves to Kubernetes. A delivery lead tracks velocity and output quality throughout.
Our Hiring Models
Dedicated Generative AI Developers
Hire dedicated generative AI developers who work only on your roadmap, full time, for the length of the engagement. This fits teams building a GenAI product who need the same people through discovery, build, and rollout.
Team Augmentation
Hire remote generative AI developers who slot into your team to close one gap, such as retrieval quality, evaluation, or serving cost. If the gap is classical ML or data pipelines instead, our dedicated AI developers cover that side.
Project-Based Engagement
Fixed scope and a fixed outcome. Useful for a proof of value, a model migration, or a four-week feasibility build that tells you whether the use case is worth funding.
Where Our Generative AI Engineers Deliver Impact
Our team serves global clients across banking and financial services, healthcare, manufacturing, retail, real estate, and logistics. The generative AI engineers for hire at Entrans bring the domain rules with them. A claims summary that cites the wrong policy clause is a compliance problem, not a bad answer. Our engineers build with that distinction in mind.
Chief Executive Officer
Chief Executive Officer
Looking to Hire Generative AI Developers Who Can Get Your Model Past the Pilot?
Book a Free ConsultationFrequently Asked Questions
What does a generative AI developer do, and how is that different from an ML engineer?
A generative AI developer builds products on top of foundation models. That means retrieval pipelines, agents, prompt logic, fine-tuning, and the evaluation that proves the output is right. An ML engineer trains and serves models and owns the infrastructure underneath. The roles overlap, and one strong engineer often covers both. Describe the use case and we will recommend the mix.
How much does it cost to hire generative AI developers?
Rates depend on seniority, location, and engagement length. Offshore and nearshore GenAI engineers price well below a US in-house hire. And the in-house figure runs higher than the salary once you add recruitment fees, onboarding, and the months a seat sits empty. Budget for inference too, since token spend can outgrow the engineering bill on a busy feature. Entrans quotes a blended rate after a scoping call, with no recruitment fee.
How fast can you onboard dedicated generative AI developers?
Curated profiles reach you within 24 to 48 hours, and approved engineers start within 48 to 72 hours. That window covers NDAs, system access, and environment setup. Our delivery spans the US, UK, UAE, and India, so you can pick engineers with real overlap on your working hours.
How do you stop the model from hallucinating in production?
You cannot remove hallucination, so we contain it. Answers are grounded in retrieved sources with citations, and an evaluation set scores groundedness on every change. Low-confidence responses either escalate to a person or decline to answer. We also red-team the prompts before launch and monitor output quality after. On one AI content platform, a human review gate held accuracy at 100 percent before publishing.
Who owns the code, prompts, and fine-tuned models, and how is our data protected?
You own all of it. Code, prompts, fine-tuned weights, evaluation datasets, and documentation are assigned to you under the engagement contract. Everything is committed to your repositories from the first sprint. Where you prefer it, the work runs inside your own cloud accounts, so your data never leaves your boundary. Entrans is ISO certified and a NASSCOM member, and every engineer signs an NDA before day one.
