Hire LangChain developers who turn LLM logic into bulletproof, composable chains. Launch smart apps that handle context, access proprietary data, and run complex workflows without errors or hallucinations.
Hire LangChain developers who go beyond simple wrappers. We build smart application layers that tame messy real-world data, linking LLMs to PDFs, SQL warehouses, and internal APIs while bridging models with business logic.
Hire dedicated LangChain developers who use LCEL to craft composable, production-ready primitives. Swap spaghetti code for declarative chains that handle streaming, retries, and fallbacks natively that keep your apps maintainable as complexity scales.
Standard context injection falls short. Our LangChain developers for hire deploy Hybrid Search (Semantic + Keyword) and Parent-Document retrieval, fine-tuning chunking and re-ranking so your LLM pulls the exact context it needs from vector stores like Pinecone or Weaviate.
Hire LangChain developers who give your models real agency. We design precise tools and function schemas that let agents query APIs, run Python code, or explore the web while making sure there is strict input and output validation.
Build architecture that avoids vendor lock-in. Whether it’s a router switching GPT-4 for logic and Haiku for speed, or swapping commercial APIs for local or cloud models like Llama 3, Mistral, or Gemini without touching your codebase.
Conversations need context. Our LangChain developers for hire build advanced memory modules Entity Memory and Summary Buffer that retain long-term history without blowing token limits, while managing session state across distributed users.
Hire LangChain developers with deep proficiency in Python/TypeScript, Vector Embeddings, and Prompt Engineering. Entrans connects you with engineers who understand the nuances of temperature, token economics, and chain latency, building systems that are fast, cost-effective, and accurate.
Stop wrestling with messy text. Hire LangChain experts to build Pydantic parsers and structured output chains, making sure your LLMs deliver clean, valid JSON or XML ready for frontend or backend integration every time.
Hire LangChain developers skilled in LangGraph. We build stateful, multi-agent workflows with cyclic logic, enabling Plan-Execute-Revise loops where AI personas critique and improve each other before final output.
You can't improve what you can't measure. Hire dedicated LangChain developers who integrate LangSmith for full observability. We set up tracing to debug latency bottlenecks, monitor token usage, and run evaluation datasets to regression-test your prompts before they hit production.
We extend LangChain beyond default integrations. Hire developers to write custom Document Loaders for your proprietary file formats or build specific Retrievers that interact with legacy enterprise search engines, ensuring no data silo is left behind.
Entrans’ specialists manage the deployment of local embeddings and LLMs using Ollama or HuggingFace pipelines. We keep your LangChain applications secure and compliant by ensuring sensitive data never leaves your VPC during the inference process.
We design AI-powered ingestion pipelines. Hire LangChain consultants skilled in Unstructured.io and document partitioning to clean, metadata-tag, and vectorize massive knowledge bases, turning raw PDFs and HTML into a queryable brain.
Technical Stack: Next.js (SSG), Strapi, Tailwind CSS, Vercel
A leading Fintech services company was limited by its slow, monolithic WordPress site, which hurt its SEO and content workflow. We migrated them to a headless architecture with Next.js for Static Site Generation and Strapi as the CMS.
The new platform had better functionality and a better UX for users uploading bills, statements, and receipts for reconciliation.
Technical Stack: Python, Flutter, Angular, LangChain, FastAPI
Enterprises needed a secure AI solution for human-like conversations grounded in their own proprietary data. We developed Thunai, our enterprise-grade agentic platform, to automate sales and support workflows.
Thunai delivers accurate, data-driven responses, handles complex agent tasks, and provides a unified support experience in over 150 languages.
Entrans provides specialized LangChain development teams that ensure architectural integrity, query relevance, and system scalability. Our pre-vetted network offers deep expertise in Python, RAG patterns, and cognitive architectures, delivered through adaptive engagement models that integrate seamlessly with your existing stack.
Identify high-impact opportunities and data accessibility gaps that need LangChain Developer roles.
Access pre-screened profiles with verified expertise in Python, Vector Stores, and Chain orchestration.
Validate expertise in LCEL syntax, retrieval optimization, and custom tool creation through technical challenges.
Onboard experts who understand your specific API documentation and cloud environment, preventing ramp-up delays.
Maintain continuity with support for library updates, prompt versioning, and latency optimization.
Ideal for building core AI products, maintaining complex RAG systems, and long-term feature development.
Scale your engineering team with skilled AI consultants who can plug into your existing Python/JS backend team.
Hire for specific tasks, such as building a semantic search engine, migrating to a new vector DB, or setting up LangSmith.
Our team serves a global clientele, specializing in Fintech, Healthcare, and EdTech applications. Our specialists architect scalable knowledge engines using their deep knowledge of the LangChain ecosystem.
A LangChain developer is a software engineer specialized in using the LangChain framework to build LLM-powered applications. They focus on the "glue code" that connects language models (like GPT-4) to other sources of data (databases, PDFs) and allows them to interact with their environment.
LangChain is the industry standard for building context-aware AI applications. It is used for chatbots that answer questions based on your documentation, extraction tools that turn text into structured data, and autonomous agents that can plan and execute workflows across multiple APIs.
LangChain is not a programming language itself, but rather a framework used to build applications powered by large language models. It was originally built in Python, which remains its most popular and feature-rich implementation. There is also a fully supported version available for JavaScript/TypeScript, allowing web developers to utilize the same powerful tools.
Raw LLMs have no memory and can't see your data. Hiring dedicated LangChain developers enables you to build apps that "know" your business. They build RAG (Retrieval Augmented Generation) systems to ground answers in facts, preventing hallucinations, and create Agents that can autonomously execute tasks.
In 2026, the demand to hire LangChain developers is high. In the US, hourly rates range between $75 and $130, with annual salaries often exceeding $150,000. Offshore talent in India provides excellent value, with proficient engineers starting around $30-$45 per hour, while senior LangChain developers in Europe or Latin America typically range from $55 to $90 per hour.