Hire AI Developers Who Get Models Out of the Lab and Into Production
Most enterprise AI stalls right after the pilot. Hire AI developers from Entrans and you get engineers who build agentic systems, LLM applications, and ML pipelines that survive real traffic. Evaluation, guardrails, and monitoring go in from the first sprint. Start with one specialist or a full pod, onboarded in 48 to 72 hours.
Why Market Leaders Choose Entrans AI Developers
Entrans has delivered 150+ AI projects for 200+ enterprises. Our engineers do not stop at a working notebook. They own the model, the pipeline, and the day it goes live.
1. Production Is the Deliverable, Not the Demo
Our AI engineers ship with evaluation sets, guardrails, and rollback plans from day one. On a healthcare payer platform, that discipline moved prior authorization processing 3X faster because the model held up under real document volume.
2. Senior Engineers, Vetted for Depth
Every AI developer clears a technical screen on model design, data handling, and deployment before you meet them. You interview finalists, not filler profiles.
3. Agentic AI, Not Just Chat Wrappers
Our engineers build multi-agent workflows with tool calling, retrieval, and state that holds across long-running tasks. Agentic AI framework integration is a core Entrans service, so this is our default rather than a side project.
4. Data and MLOps Under One Roof
Bad data kills more AI projects than bad models. Our AI developers sit beside data engineers who own pipelines, feature stores, and drift monitoring, so nothing gets thrown over a wall.
5. Flexible Contracts, Full Ownership
Hire an AI consultant for a six-week assessment or a dedicated pod for two years. Scale the team as the roadmap changes, and keep ownership of the code, prompts, and model weights.
Hire AI Developers From Entrans Who Are Certified and Experienced
Here is what our AI developers for hire actually do in the first 90 days, on live enterprise systems with real compliance constraints.
LLM Application Engineering
Build retrieval pipelines with chunking strategies, embeddings, and vector search that return grounded, cited answers. Includes prompt versioning and fallback handling when retrieval comes back empty.
Agent and Workflow Orchestration
Design multi-step agents with tool calling, memory, retries, and human approval gates, the same pattern behind our AI agent development solution. Wire them into the ERP, CRM, and ticketing systems your team already runs.
Model Fine-Tuning and Adaptation
Adapt open and hosted models with LoRA, PEFT, and instruction tuning when prompting alone misses the accuracy bar. Every change is benchmarked on your own data, before and after.
Document AI and Computer Vision
Pull structured fields out of PDFs, scans, and images using OCR plus language models. Our team held 95%+ extraction accuracy on a growing healthcare document load.
MLOps and Production Deployment
Package models with Docker, serve them on SageMaker, Vertex AI, or Kubernetes, and automate retraining. Versioning, CI/CD, and rollback come standard, backed by our DataOps and MLOps services.
Evaluation, Guardrails, and Monitoring
Build eval harnesses, red-team prompt suites, PII redaction, and drift alerts so you know the moment quality slips. Most teams skip this, and it is the main reason pilots quietly die.
Schedule Interviews With AI Engineers and Onboard Them Within 48 to 72 Hours
AI Development Technology Expertise
Models and Frameworks
Agentic AI, LLM, and Retrieval
Data, Cloud, and MLOps
Evaluation, Delivery, and Governance
Our Customer Success Stories
Prior Authorization Automation for a Healthcare Payer Platform
Industry: Healthcare and payer operations
Technical Stack: OCR, large language models, rule engine, microservices architecture, automated testing framework, structured PDF reporting
Authorization requests arrived as incomplete, fragmented data, and every submission was validated by hand. Approvals dragged and error rates climbed as volume grew. Our engineers rebuilt prior authorization automation around AI data extraction using OCR and large language models. A rule engine then checks each request against medical and payer requirements. The monolith was split into microservices, an automated testing framework covered unit, integration, and system layers, and every decision produced a structured PDF report.
Results: 3X faster processing, 70% less manual effort and fewer errors, and 95%+ data extraction accuracy sustained as document volumes grew.
AI Invoice and GRN Reconciliation Platform
Industry: Finance and procurement
Technical Stack: AWS Bedrock, MongoDB, PostgreSQL, Flask
The finance team reconciled invoices, purchase orders, and goods receipt notes by hand. The documents arrived as PDFs, scans, and structured records with no common extraction path, so payment approvals slipped. Entrans built an AI invoice and GRN reconciliation platform that extracts invoice data with LLMs on AWS Bedrock. It matches each invoice against its PO and GRN, then flags gaps in quantity, pricing, and supplier detail. MongoDB holds the documents, PostgreSQL holds the transactional records, and a Flask application runs the workflow and reporting.
Results: 80% reduction in manual reconciliation effort and 2X faster payment processing, across PDFs, scans, and structured records.
Designed for Enterprise Speed and Control
Hire an AI developer 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 actually need. That might be an LLM engineer, an ML engineer, or an AI consultant to scope the problem first.
2. Get Curated Profiles (Within 24 to 48 Hours)
You receive a short list of pre-vetted AI developers whose project history matches your domain. No stack of resumes to sort through.
3. Evaluate and Interview
Run your own technical round, a live task, or a short paid trial. Ask candidates about a model that failed and what they changed. We will help you shape the questions if that helps.
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 pipelines grow or a DevOps engineer when you move to Kubernetes. A delivery lead tracks velocity and quality for the length of the engagement.
Our Hiring Models
Dedicated AI Developers
Hire dedicated AI developers who work only on your roadmap, full time, for the length of the engagement. This fits teams building an AI product who need the same people through discovery, build, and rollout.
Team Augmentation
Hire remote AI developers who slot into your existing team to close one specific gap, such as retrieval quality, evaluation, or MLOps. They follow your process, your tooling, and your code review standards.
Project-Based Engagement
Fixed scope and a fixed outcome. Useful for a proof of value, a model migration, or a six-week assessment. Hire an AI consultant to size the opportunity before you commit headcount.
Where Our AI Engineers Deliver Impact
Our team serves global clients across banking and financial services, healthcare, manufacturing, retail, real estate, and logistics. The AI engineers for hire at Entrans carry domain context into the model. That is why a fraud score, a claims decision, and a demand forecast never get built the same way.
Chief Executive Officer
Chief Executive Officer
Looking to Hire AI Developers Who Can Take Your Models to Production?
Book a Free ConsultationFrequently Asked Questions
How much does it cost to hire AI developers?
Cost depends on seniority, location, and engagement length. Offshore and nearshore AI developers generally price well below a US in-house hire. And the in-house number runs higher than the salary once you add recruitment fees, onboarding, benefits, and the months a seat sits empty. Entrans quotes a blended rate per engineer after a short scoping call, with no recruitment fee and no long lock-in. Send us the use case and we will put an estimate in writing.
What does an AI developer do, and how is that different from an ML engineer?
An AI developer builds intelligent features into products, including LLM applications, retrieval pipelines, agents, and the prompts and evaluations around them. An ML engineer focuses on the infrastructure that trains, serves, and monitors models at scale. The two roles overlap heavily, and on smaller teams one person covers both. Describe your use case and we will recommend the right mix.
How fast can you onboard dedicated AI developers?
You get curated profiles within 24 to 48 hours and onboarded engineers within 48 to 72 hours of approval. That window covers NDAs, system access, and environment setup. Our delivery runs across the US, UK, UAE, and India. So you can pick engineers with real overlap on your working hours, not a handover email each morning.
Who owns the code, models, and data when we hire remote AI developers?
You do. Code, prompts, fine-tuned weights, 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 happens inside your own cloud accounts and access controls. Entrans is ISO certified and a NASSCOM member, and every engineer signs an NDA before day one.
Should I hire a dedicated AI developer or an AI consultant?
Hire an AI consultant when you are still deciding what to build. A four to six week assessment covers use case selection, data readiness, and the build-versus-buy call. It usually costs less than one wrong first sprint. Hire dedicated AI developers once the use case is settled and you need sustained delivery. Plenty of Entrans clients start with dedicated AI consultants and convert the same people into the build team.
