Hire LLM Developers
Our experts have helped dozens of companies ship production-ready solutions powered by large language models. Get dedicated LLM developers on your team in as little as 2 weeks. Fully vetted and time zone-aligned.
4.9/5
60 client reviews
Why choose between cost savings and quality?
We give you both.
We're a US-based company powered by LATAM dev teams. It's a powerful combination. Procurement is simpler. Quality expectations are shared. Accountability is always there.
Plus, our developers work your hours, speak English, and have experience with US teams. So you get the cost and scalability benefits of nearshore software development - without any of the sacrifices.
“Their engineers perform at very high standards. We've had a strong relationship for almost 7 years.”
The easy way to hire the highest quality LLM developers.
We’re a development partner, not a platform. This means we handle everything from recruitment to hardware to certifications. Work with us and enjoy the ease of a white-glove hiring experience.
We have 100s of LLM devs on staff.
We have 100s of LLM devs on staff.
Meet the LLM developers behind our best work.
This is the level of talent we place on every team. With 8+ years of experience and dozens of projects under their belts, our software engineers raise the bar on every project they work on.
Daniela developed LLM-powered document intelligence tools for the banking industry, using GPT-based models with retrieval-augmented generation to parse contracts, compliance manuals, and KYC/AML documents. Her solutions reduced manual review time for compliance teams and improved accuracy in regulatory reporting.
Javier built domain-specific LLM solutions for healthcare providers, combining GPT-4 with retrieval systems designed to meet HIPAA compliance requirements. His work streamlined clinical documentation workflows, resulting in reduced reporting time and improved accuracy.
Valeria developed multilingual summarization tools for media companies, fine-tuning transformer models such as T5 and BART on domain-specific news datasets. She built scalable pipelines with Airflow and Hugging Face Transformers, using ROUGE and BERTScore to evaluate output quality. Her work enabled editorial teams to surface insights from large volumes of content in near real time.
Martín created enterprise coding assistants using Code Llama and GPT-4, integrated directly into VS Code. He engineered custom prompt frameworks and output filtering with linting tools to match internal coding standards, reducing refactoring workload for development teams.
Daniela developed LLM-powered document intelligence tools for the banking industry, using GPT-based models with retrieval-augmented generation to parse contracts, compliance manuals, and KYC/AML documents. Her solutions reduced manual review time for compliance teams and improved accuracy in regulatory reporting.
Dozens of LLM-powered projects delivered.
Our track record means you get AI solutions and systems that meet the highest technical and business standards.
Our client needed to automate the time-consuming task of summarizing lengthy legal transcripts. We built an AI tool that capable of summarizing 200–300 pages in under 4 seconds. The tool anonymizes sensitive data, returns editable Word and PDF files, and includes hyperlinks to retain source visibility. It automatically segments text and feeds it into an NLP engine, significantly accelerating turnaround time.
This client is creating a development environment for building and testing AI pipelines with LLMs. We provided full-stack engineering support to improve performance, scale, and user experience. Our team worked on intuitive front-end components and scalable back-end services designed to handle experimentation and monitoring. These improvements helped simplify LLM pipeline prototyping and speed up iteration cycles.
This logistics company uses AI/ML to streamline catalog classification and manage cloud spending. We built a hierarchical classification model using Amazon labels and Gemini, cutting costs from $30,000 to $300 per million classifications and reducing latency from 40 seconds to 1.5 seconds. Our team improved tax classification accuracy to 95% with RAGFusion and semantic chunking. We also migrated models to GCP and automated MLOps workflows, reducing overall cloud costs by 80%.
Need extra LLM expertise?
Plug us in where you need us most.
We customize every engagement to fit your workflow, priorities, and delivery needs.
Staff Augmentation
Get senior, production-ready developers who integrate directly into your internal team. They work your hours, join your standups, and follow your workflows—just like any full-time engineer.
Dedicated teams
Spin up focused, delivery-ready pods to handle full builds or workstreams. Together we align on priorities. Then our tech PMs lead the team and drive delivery to maintain velocity and consistency.
Software outsourcing
Hand off the full project lifecycle, from planning to deployment. You define the outcomes. We take full ownership of the execution and keep you looped in every step of the way.
We don’t settle for anything less than the best, and neither should you. Our long, rigorous vetting process ensures only top performers work on your software development projects.
Get LLM results you can stand behind.
How we find the best-fit devs for your LLM projects
With a deep bench of full-time generative AI engineers, we focus on one thing: finding the right fit. We bring in senior developers who’ve worked in teams like yours and built solutions like yours.
Verified LLM Developers
Every engineer we field passes an LLM-focused evaluation that goes far beyond standard coding tests. We simulate real-world problems like prompt drift in multi-turn conversations, latency from vector-store lookups, token limit constraints, and compliance checks for model outputs. These tests show how candidates handle LLM-specific challenges. Only those with deep LLM system knowledge and strong engineering judgment make the cut.
That means you get developers who can design LLM systems that behave reliably in production, minimize hallucinations, and deliver fast, accurate responses.
Relevant LLM Project Experience
We don’t just match based on titles or tech stacks. We look at the full context of your project. That includes your model goals, infrastructure, data sensitivity, and user-facing behavior. Then we staff engineers who’ve worked on similar LLM systems. That might mean optimizing a RAG pipeline, reducing latency in chat applications, or navigating compliance in AI outputs.
Because we maintain a curated bench of full-time engineers, we can find highly qualified LLM talent in days, not months. This direct experience is critical, as it allows your team to bypass the painful learning curve associated with LLM-specific issues like prompt fragility and latency management.
Full AI Teams for LLM Projects
The most successful LLM projects are delivered by teams with the right mix of skills. We build remote, cross-functional teams that combine LLM developers with complementary AI specialists: MLOps engineers, data scientists, retrieval engineers, and model evaluation experts. All in one place.
This gives you faster ramp-up, cleaner handoffs, and consistent architecture from prototype to production. We handle retention and scaling, while you work with a single point of contact. Knowledge stays with the team, velocity builds over time, and your LLM projects move forward without stalling at each stage.
Industry Experience
An LLM that drafts product descriptions isn’t built the same way as one that processes claims or routes support tickets. Each industry brings its own data, risks, and requirements — and that shapes everything from prompt design to evaluation metrics. We draw from a broad bench of LLM engineers with sector-specific experience, so your team doesn’t waste cycles explaining how your industry works.
This experience shortens ramp-up time and sharpens decision-making. It also leads to better technical choices, from selecting the right retrieval strategy to setting up fallback logic and guardrails that reflect real-world constraints.
Ownership Mindset
Our engineers take responsibility for project outcomes, not just lines of code. That means monitoring performance, refining prompts, and retraining pipelines as user behavior evolves. Like in-house team members, they flag risks early, suggest architectural improvements, and evolve the system for long-term performance.
They also stay on top of fast-moving model changes — from prompt behavior shifts to new tool APIs — and proactively adapt systems to take advantage of new capabilities without introducing risk. This kind of ownership is what keeps your LLM solutions stable and effective long after launch.
Every engineer we field passes an LLM-focused evaluation that goes far beyond standard coding tests. We simulate real-world problems like prompt drift in multi-turn conversations, latency from vector-store lookups, token limit constraints, and compliance checks for model outputs. These tests show how candidates handle LLM-specific challenges. Only those with deep LLM system knowledge and strong engineering judgment make the cut.
That means you get developers who can design LLM systems that behave reliably in production, minimize hallucinations, and deliver fast, accurate responses.
The expertise you need for the results you want.
We've been refining our hiring process for over a decade. We can proudly say our LLM developers are the best of the best: top engineers who’ve proven they have the skills to build stable, high-performing systems.
Put top talent on your team in 2-4 weeks.
We have reps across the US.
Speak with a client engagement specialist near you.
Tell us more about your needs. We’ll discuss the best-fit solutions and team structure based on your success metrics, timeline, budget, and required skill sets.
With project specifications finalized, we select your team. We’re able to onboard developers and assemble dedicated teams in 2-4 weeks after signature.
We continually monitor our teams’ work to make sure they’re meeting your quantity and quality of work standards at all times.
Global companies have trusted our developers to build and scale custom AI solutions for over a decade.
Excellence.
Our minimum bar for client delivery.
Over 130 awards, accolades, and achievements showcase our quality and commitment to client success.
What tech leaders ask us about hiring LLM developers:
What kinds of projects have your LLM developers shipped?
Our teams have rolled out sentiment analysis tools, handled LLM fine-tuning for knowledge bases, built retrieval-augmented virtual assistants, and other LLM projects across industries. We’ve delivered predictive modeling pipelines that pair deep learning techniques with traditional data analysis to surface insights for executive dashboards. Our AI solutions span finance, healthcare, and logistics, so it is likely we’ve solved challenges similar to yours.
How do you hire LLM engineers? How do you evaluate technical skills?
We evaluate LLM engineers through practical, scenario-based assessments modeled on real delivery work. Each candidate is tested on their ability to design prompts, debug LLM outputs, reason through architectural tradeoffs, and work with retrieval, latency, and compliance constraints — all under realistic time pressure.
But we don’t stop at code. We also assess how they communicate decisions, collaborate across roles, and stay grounded in product outcomes. Our goal isn’t just to test theoretical knowledge. It’s to find engineers who can contribute to stable, performant LLM systems in a real-world environment and who can work seamlessly with your product and engineering teams.
Do your LLM engineers support deployment across different cloud computing platforms, languages, and development tools?
Yes. Our LLM engineers have deployed systems on AWS, Azure, and Google Cloud, using each platform’s native services. They work comfortably with tools like Terraform, Docker, and Helm, and plug into your CI/CD workflows without friction.
While Python is the go-to for LLM work, we’ve also shipped services in Go, TypeScript, and Java when needed for performance or system alignment.
When the work calls for more than one role, we can bring in MLOps, DevOps, or platform engineers from our bench to round out the team — so you get exactly the expertise you need to go from prototype to production without slowing down.
How quickly can we onboard top LLM developers, and what does the hiring process look like?
We’ll share qualified profiles within a few days, and most clients are onboarding engineers in about two weeks.
We’ve already thoroughly vetted every professional on our team, so there’s no need to run interviews (unless you want to). Our direct placement model is highly effective (96% on the first try and 99% on the second). In fact, most clients prefer it for speed and simplicity.
We also handle all the hiring logistics, including equipment, software setup, necessary certifications, documentation, and more.
What safeguards do you put in place for data handling and regulatory compliance?
We isolate PII at the schema level and encrypt data in transit and at rest. Access logs feed into a tamper-evident store for real-time audit readiness. Our secure pipelines support data science workflows on sensitive data without adding friction to compliance or audit processes. Our delivery managers have cleared HIPAA, SOC 2, and GDPR reviews, which means your legal team sees proven controls and doesn’t have to handle special exceptions.
Do you provide ongoing optimization like fine-tuning models and performance monitoring?
Yes. Fine-tuning language models on fresh feedback is part of the build. Our teams monitor how models behave in production, test new prompts in shadow mode, and optimize model performance through batching, routing, and distillation. Dashboards map token usage so finance can track spend, while alerts catch drift before users notice. You get continuous improvement, not a one-and-done project.
What if we need to scale our LLM development team? Can you support it?
Absolutely. With a 4,000-person engineering bench and deep internal specialization, we can scale from a single engineer to a full program without disrupting velocity. If your roadmap shifts toward multimodal AI development or heavier data analytics, we can bring in specialists from those domains. Regardless of what roles you need, we apply the same talent logic and delivery structure to keep momentum high and knowledge intact as the team grows.
How do you control cost while maintaining quality in large language model development?
We benchmark model sizes against business metrics, then route low-stakes queries to smaller engines and reserve high-value calls for premium options. Caching, quantization, and elastic autoscaling cut idle GPU spend. Weekly cost reviews with your stakeholders keep the budget transparent, and when thresholds approach, we propose scalable solutions before overruns happen.
Have you worked with companies like ours, and what valuable insights can you bring?
Since 2009, we have partnered with global enterprises across 130+ industries. We were creating advanced deep learning, computer vision, and natural language processing (NLP) solutions long before the LLM boom. Today, our LLM solutions power e-commerce, support ticketing systems, and manage knowledge bases for large organizations. Our engineers don’t just understand the tech; they understand how it fits into your business.
See why the biggest names in tech build with our LLM developers.
Let's Discuss Your LLM Project
