Hire LLM Engineers Who Ship to Production, Not to a Demo
A prototype that answers three questions is not a product. Hire LLM developers from Entrans who build the retrieval layer and the evaluation harness. They also own the cost controls that keep an AI feature working at real volume. Interview this week and onboard in 48 to 72 hours.
Why Market Leaders Choose Entrans LLM Engineers
The LLM talent market has a supply problem in disguise. Thousands of engineers have called a model API. Very few have run one in production through a model deprecation, a cost spike, and a security review.
1. Applied Engineers, Not Research Scientists
You do not need a PhD to ship an AI feature. Most hiring pages describe a research role: training models from scratch and publishing papers. Our engineers build on GPT, Claude, and Llama, and they know when a better prompt beats a fine-tune.
2. Production Evidence on Every Profile
You pick from a bench of 500+ domain-trained professionals, backed by 150+ delivered AI projects. Every profile shows a system real users depended on, not a weekend project.
3. Cost and Latency Are Treated as Requirements
An AI feature that works but burns your margin is still a failed feature. Our engineers route simple calls to cheaper models, add caching, and report token spend per feature so finance stops guessing.
4. A Full Engineering Team Stands Behind Each Engineer
Marketplaces sell you a person and stop there. Behind your engineer sit our data, cloud, and generative AI consulting practices. A hard problem gets escalated instead of stalling for a week.
5. Private and On-Prem Deployment When Data Cannot Leave
Regulated teams often cannot send prompts to a public API. Our engineers serve open-weight models inside your own enterprise cloud environment or data center, with the same evaluation and monitoring discipline.
Hire Expert LLM Developers From Entrans Who Are Certified and Experienced
Here is what these engineers own once they join your sprint. They sit next to the team that handles agentic AI framework integration. A feature that grows into a multi-step agent has somewhere to go next.
RAG and Retrieval Architecture
Chunking strategy, embedding choice, hybrid search, and reranking so answers come from your documents. Every response carries a citation your users can check.
Prompt Engineering and Structured Output
Few-shot and chain-of-thought design, JSON schema enforcement, and function calling that returns data your code can trust. Prompts live in version control, not in someone’s notebook.
Fine-Tuning and Model Selection
LoRA and QLoRA tuning on curated domain data when prompting hits its ceiling. Your engineer also tells you when a fine-tune is the wrong answer, which saves more money than the tune would.
Evaluation and Regression Testing
Golden test sets, LLM-as-judge scoring, and CI gates that block a prompt change if quality drops. You learn about a regression from your pipeline, not from a customer.
Inference Cost, Latency, and Model Routing
Prompt caching, batching, streaming, and routing across model tiers by task difficulty. Token budgets get set per feature and tracked, so spend stays predictable as traffic grows.
Guardrails, Privacy, and Governance
PII redaction before anything reaches a prompt or a log, prompt injection defense, output filtering, and audit trails. Our engineers have delivered under HIPAA, GDPR, SOC 2, and PCI DSS requirements.
Schedule Interviews With LLM Engineers and Onboard Them Within 48 to 72 Hours
LLM Engineering Technology Expertise
Models and Providers
Orchestration and Agents
Retrieval and Data
Serving, Evaluation, and Ops
Our Customer Success Stories
LLM and OCR Automation for Healthcare Prior Authorization
Industry: Healthcare and payer operations
Technical Stack: Large language models | AI-powered OCR | Configurable rule engine | Microservices architecture | Automated testing framework | Structured PDF reporting
A healthcare platform managing prior authorization ran on incomplete data spread across several stakeholders, and every submission needed manual validation. Approvals dragged. Entrans built the prior authorization automation using OCR and large language models to pull fields out of unstructured documents. A configurable rule engine then validates them against medical and payer requirements. Our team also moved the platform from a monolith to microservices and added an automated testing framework. Processing runs 3X faster, manual effort and errors dropped 70%, and extraction accuracy holds above 95% as document volume grows.
AI Invoice and GRN Reconciliation on AWS Bedrock
Industry: Procurement and enterprise finance
Technical Stack: LLM functions via AWS Bedrock | MongoDB | PostgreSQL | Flask | Multi-database architecture
A procurement-focused enterprise was reconciling high volumes of invoices by hand. Documents arrived as PDFs, scans, and structured records with no standard extraction, so finance compared fields against purchase orders and goods receipt notes manually. Entrans built an invoice and GRN reconciliation platform that uses LLM functions on AWS Bedrock to extract key fields across every format. It then flags quantity gaps, pricing mismatches, missing line items, and wrong supplier details automatically. Manual reconciliation effort fell 80% and payment processing runs 2X faster.
How to Hire LLM Developers Without Losing a Quarter
Hiring an LLM engineer should take days, not months. Our process runs fast and you approve every step.
1. Share Your Requirements
Share the use case, your stack, and expected volume. One 30-minute call is enough.
2. Get Curated Profiles (Within 24 to 48 Hours)
You get matched profiles with shipped LLM work in RAG, fine-tuning, or agents. No blind bench dumps.
3. Evaluate and Interview
Interview on your own terms. Bring a prompt that fails today and see how the engineer debugs it.
4. Onboard and Kickoff (Within 48 to 72 Hours)
NDAs and model access are sorted before day one. Your engineer joins standups and starts on your costliest call path.
5. Continuous Support and Scaling
Add a data engineer, MLOps engineer, or evaluation specialist as scope grows. A delivery lead reviews the work every sprint.
Our Hiring Models
Dedicated LLM Developers
One engineer, or a small pod, working only on your AI features for the long run. They own retrieval quality, the eval suite, and the cost profile. Hire remote LLM engineers on contract-to-hire or project terms.
Team Augmentation
Drop an LLM engineer into the product team you already run. This fits when you have prompt engineers or backend engineers in place and need retrieval, tuning, and evaluation depth added.
Project-Based Engagement
Scoped work with a fixed outcome: a RAG build, an evaluation harness for a feature you already shipped, or a private model deployment. You get the deliverable and the documentation, then decide what comes next.
Where Our LLM Engineers Deliver Impact
We work with global clients in document-heavy, regulated sectors: banking, insurance, healthcare, manufacturing, retail, and logistics. Our specialists build AI features that hold up under audit and real volume. They draw on deep work in retrieval, document extraction, and model governance.
Chief Executive Officer
Chief Executive Officer
Looking to Hire an LLM Engineer Before Your Next AI Release?
Book a Free ConsultationFrequently Asked Questions
How do you hire LLM developers who can actually ship?
Screen for production evidence, not tool familiarity. Ask for a feature the candidate shipped that real users depended on, then ask what broke and how they found out. The strongest signal is an evaluation pipeline, because that is what separates an engineer who holds quality steady from one who only ships demos. Then decide quickly, since senior LLM engineers usually hold more than one offer.
What does an LLM engineer do that a machine learning engineer does not?
An LLM engineer builds products on top of foundation models rather than training models from scratch. The daily work is retrieval architecture, prompt and output design, evaluation harnesses, inference cost control, and guardrails. A machine learning engineer is closer to data pipelines, feature engineering, and model training. Teams shipping an AI feature usually need the first profile, not the second.
How much does it cost to hire an LLM developer?
Cost depends on seniority, scope, and engagement model rather than one rate card. A US full-time hire means senior-level total compensation plus months of search. Published market ranges for senior LLM engineers in the US run well into six figures. A dedicated engineer through Entrans starts within days and scales by sprint. Send us your use case and we will come back with a written estimate.
Can you run an LLM privately on our own infrastructure?
Yes. When prompts cannot leave your environment, our engineers serve open-weight models such as Llama or Mistral in your own cloud account or data center. That covers GPU sizing, inference serving, autoscaling, and access control. You keep the same evaluation and monitoring discipline you would get with a hosted API, without sending data to a third party.
Can you fix an LLM feature we already built?
Yes, and it is a common starting point. Our engineers audit the existing stack for accuracy, latency, and cost problems. Then they fix the retrieval layer, rewrite prompts against a test set, and add caching or model routing where spend is highest. You get a written baseline before the work starts, so the improvement is measurable rather than asserted.
