Anyone can bolt an API onto a chatbot. CodesClue engineers build LLM systems that actually understand your business and get results that show up on your bottom line.
We hire and deploy senior LLM engineers who go far beyond plug-and-play integration. Our team designs intelligent, context-aware AI fine-tuned models, production-grade RAG pipelines, custom chatbots, AI copilots, and enterprise automation that fits seamlessly into how you already work.
Need an LLM specialist to architect your AI strategy? An integration engineer to connect models into your existing stack? A custom LLM engineer to build something no off-the-shelf tool can do? We match the right expertise to your roadmap not a generic dev, but the specific skill set your project demands.
Partner with a Generative AI team that ships. We turn powerful language models into secure, scalable products built to perform, built to last, built to deliver real business value.
8 Hours/day
4 Hours/day
Pay as you go
Enterprise AI needs more than a powerful language model, it needs the right data, context, engineering, and safeguards behind it. Hire LLM developers at CodesClue to transform foundation models into practical, production-ready solutions.
From fine-tuning and knowledge retrieval to prompt optimization and generative AI development, our specialists help businesses create reliable, scalable, and purpose-built LLM applications that fit seamlessly into existing products, workflows, and technology ecosystems.
We improve your foundation model for specialized tasks where general-purpose prompting isn't enough.
Our engineers prepare domain-specific datasets, select efficient fine-tuning approaches such as PEFT and LoRA/QLoRA, establish evaluation criteria, and compare model performance against your business requirements.
Give your AI applications access to the information your business actually relies on.
Our LLM integration engineers design retrieval pipelines that connect LLMs with documents, databases, APIs, and knowledge bases, helping applications retrieve relevant context before generating an answer.
Move beyond trial-and-error prompting with structured evaluation. Our LLM specialists design reusable prompt architectures, test outputs against defined quality criteria, identify failure patterns, and implement guardrails to improve consistency across different users, inputs, and use cases.
Move beyond trial-and-error prompting with structured evaluation.
Our generative AI developers design reusable prompt architectures, test outputs against defined quality criteria, identify failure patterns, and implement guardrails to improve consistency across different users, inputs, and use cases.
Our LLM development services help businesses turn language models into practical, scalable AI solutions. From custom models and NLP to intelligent assistants, analytics, and multimodal applications, our LLM developers build solutions aligned with your data, workflows, and business goals.
Build domain-specific LLM applications around your business data, workflows, terminology, and product requirements. We help you move from model selection and architecture to development and deployment.
Apply language intelligence to real business workflows, including document classification, semantic search, summarization, entity extraction, sentiment analysis, and conversational experiences.
Combine language and visual intelligence to process documents, images, charts, screenshots, and other visual information alongside text.
Build conversational systems that can answer questions, retrieve information, execute actions, and assist users across websites, applications, and enterprise platforms.
Connect LLMs with structured business data to let users ask questions in natural language and receive understandable summaries, insights, and recommendations.
Develop production-ready applications using LLMs and other generative AI technologies, with integrations across APIs, databases, cloud services, and enterprise systems.
An LLM engineer bridges the gap between foundation models and real-world applications. They design the technical systems around an LLM, connect models to business data, evaluate output quality, optimize performance, and prepare AI applications for reliable production use.
Key Responsibilities:
We work with modern, scalable, and industry proven technologies across frontend, backend, mobile, cloud, and database systems. Our team selects the right stack based on your business requirements, performance expectations, and future scalability needs. From web and mobile frameworks to cloud infrastructure and DevOps tools, we build solutions that are secure, flexible, and growth ready.
| Front-End | React.js Next.js Angular Vue.js Nuxt.js HTML CSS Bootstrap JavaScript TypeScript Tailwind CSS |
| Back-End | Node.js Ruby on Rails (RoR) Laravel Django Java Python PHP Express.js .Net Core NestJS |
| Database | MongoDB MySQL PostgreSQL SQLite Firebase Redis |
| Mobile Development | Flutter iOS Android React Native |
| UI/UX | Figma Illustrator Photoshop Sketch |
| Business Intelligence | Tableau Power BI |
| IoT | AWS IoT Core Azure IoT Hub Google Cloud IoT Core IBM Watson IoT Raspberry Pi MQTT Arduino |
| Automation | UiPath Power Automate Automation Anywhere |
| AI & ML | TensorFlow PyTorch Keras Scikit-learn OpenCV AWS AI Services IBM Watson Microsoft CNTK NLTK Evidently AI |
| AI/ML Tools | AI Agents Jupyter Anaconda PySpark Caffe2 GitHub Copilot ChatGPT |
| AI & LLM Models | GPT-4 GPT-3.5 GPT-3 LLaMA 3 LLaMA 2 DALL·E PaLM 2 Whisper Bard Midjourney Claude BERT |
| Cloud | AWS Microsoft Azure Google Cloud Platform Docker Kubernetes Terraform Jenkins Ansible |
| Testing & QA | Selenium JUnit TestNG Cucumber Postman JMeter SonarQube TestRail Cypress |
Enterprise AI initiatives need to work within existing technology environments, not exist as isolated experiments. CodesClue helps organizations bring specialized LLM engineering expertise into their teams to design, integrate, deploy, and improve AI systems around real business requirements.
Build your LLM team without getting stuck in a lengthy hiring cycle. Our streamlined process connects you with specialists who understand your AI use case, technical environment, and product goals, so you can start building, testing, and improving faster.
Share your AI use case, data sources, model preferences, integration requirements, and business objectives. We map your needs to the right LLM expertise.
Review hand-picked LLM developers with relevant experience in RAG, fine-tuning, prompt engineering, integrations, and generative AI. Interview candidates and choose the expertise that fits your project.
Once you select your engineer, we make onboarding straightforward. Your LLM specialist integrates into your existing product, engineering, and communication workflows with minimal disruption.
Your AI journey doesn't end at deployment. Continuously improve model quality, response accuracy, latency, and cost as your users, data, and business requirements evolve.
Leading start ups, SMEs, and large scale organisations have trusted us for their software development project requirements.
Was great to work with Ketan. Always optimistic, very professional and hard worker. Knew how to solve complex problems. Great project manager. Recommend them for web development.
Communication was a key part of this any new features and updates were handled with care and done in a promptly manner. While what we were asking for was not always worded in the correct manner for the I.T. space they always understood what we were asking for.
A very client centric company with incredible top executives. They have years of experience and outstanding knowledge.
Never faced an issue with scheduling meetings, or change notifications.
CodesClue Technologies delivered a high-quality product that met the client's expectations. The team maintained high professionalism and clear communication throughout the engagement. Moreover, they were highly responsive to the client's needs and proactive in problem-solving.
CodesClue Technologies built a secure, user-friendly platform for managing digital legacies on NextLifeBook. The team ensured seamless functionality, strong data security, and an intuitive experience. Despite challenges, their dedication and attention to detail delivered a meaningful product that helps users plan and preserve their legacy effectively.
Want to know more about Codesclue? These Frequently Asked Questions might help.
A general AI developer may work across machine learning, computer vision, predictive analytics, and automation. An LLM specialist focuses specifically on language-model applications, including RAG, fine-tuning, prompt engineering, evaluation, AI agents, and LLM deployment. This specialized expertise helps build more reliable and context-aware language solutions.
An API can get you started, but production LLM applications often require customization, data integration, security controls, evaluation, and optimization. When you hire an LLM developer, you gain expertise to adapt models to your workflows, connect proprietary data, improve response quality, manage costs, and build a solution around your actual business requirements.
Our LLM integration engineers work with modern frameworks and tools including LangChain, LlamaIndex, Hugging Face Transformers, OpenAI APIs, Anthropic APIs, vector databases, and cloud platforms. The technology stack depends on your use case, existing infrastructure, model requirements, and integration goals.
You can engage a custom LLM engineer for hire based on your project’s scope and duration. Options can include hourly, monthly, or fixed-project engagements. This flexibility allows you to bring in specialized expertise for a specific RAG implementation, model optimization project, AI product, or longer-term development roadmap.
Yes. Our LLM development services can support model customization and fine-tuning for domain-specific requirements. We can work with proprietary or open-source models and help with data preparation, parameter-efficient fine-tuning, evaluation, prompt optimization, and deployment based on your application’s requirements.
The best model depends on factors such as task complexity, context requirements, accuracy, latency, privacy, deployment options, integration requirements, and cost. An LLM engineer can benchmark suitable models against representative workloads rather than selecting a model based only on general benchmark scores.