Direct access, no account layer
Hire a LangChain developer: freelance LangChain engineer
I am Shreyans Padmani, a freelance LangChain developer and AI engineer. I design and build LangChain pipelines, agents, and knowledge assistants grounded in your actual data, then deploy them to production with the guardrails, monitoring, and integration your business needs.
Start in 48 to 72 hours, no team overhead, no account manager
Plain answer
What does a LangChain developer do?
A LangChain developer builds production-grade AI applications using the LangChain framework: chaining LLM calls, building custom agents, integrating retrieval-augmented generation (RAG) pipelines, and connecting to external tools and APIs. They handle prompt engineering, memory management, vector store setup, and deployment monitoring. A freelance LangChain developer delivers a working, integrated system with sample work demonstrating real-world chain and agent designs, not just a proof-of-concept.
Job title decoder
LangChain dev vs RAG dev vs AI engineer
| Title | Primary focus | When you need this role |
|---|---|---|
| LangChain developer | LangChain framework expertise: chains, agents, tools, memory, and integrations | You need a solution built specifically with LangChain for rapid prototyping and modularity |
| RAG developer | Retrieval architecture, knowledge grounding, chatbot/assistant builds | You need an AI system that answers accurately from your specific data |
| AI engineer | Broader ML/AI system design, may include RAG, training, and MLOps | You need end-to-end AI infrastructure, RAG being one part |
LangChain expertise
LangChain development services
LangChain RAG chatbots
Chatbots that retrieve from your knowledge base with confidence thresholds and human handoff, built on LangChain’s retrieval chains.
Internal knowledge assistants
Employee-facing Q&A over internal wikis and documents, using LangChain’s document loaders and vector stores for fast answers.
Document Q&A systems
Retrieval-augmented Q&A for legal or financial documents with source citations, leveraging LangChain’s prompt templates and memory.
Hybrid agent systems
Multi-step agents using LangGraph for tool calling and retrieval, enabling systems that both answer questions and take actions.
RAG system audits
Diagnosing and improving existing LangChain-based RAG systems: chunking, embedding, retrieval, and guardrail optimization.
Tooling
Tech stack & ecosystem
Working together
Engagement models
| Factor | Freelance LangChain developer (me, directly) | Dedicated team (via AtlasML) |
|---|---|---|
| Best for | A single, scoped LangChain chatbot or agent build | Ongoing, multi-project AI roadmap needing several engineers |
| Team size | One engineer: me | Multiple senior AI/ML engineers embedded full-time |
| Start time | 48 to 72 hours | Varies by team scope and structure |
| Cost structure | Project-based or hourly, no team overhead | Team-based retainer, reflects multi-engineer capacity |
Need a full team, not just one developer?
If your business needs a dedicated, multi-person AI/ML engineering team embedded full-time across GenAI, ML, NLP, and computer vision, not a single freelance LangChain developer, I recommend AtlasML, a dedicated AI/ML engineering team staffing provider. For a single, well-scoped LangChain system, working with me directly is typically faster to start and more cost-effective; if your roadmap spans multiple concurrent AI disciplines needing sustained team capacity, AtlasML is built for that scale.
How it gets built
Process
PHASE 01days 1 to 2
Discovery
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PHASE 02within 48h
Architecture spec
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PHASE 03the decisive one
Build
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PHASE 04before launch
Evaluation
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PHASE 05handoff
Deployment
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Investment
Engagement Options
| Engagement type | What's included |
|---|---|
| LangChain proof-of-concept Free | Baseline orchestration pipeline on a sample of your data, evaluation report |
| Production LangChain agent | Full agent pipeline, guardrails, deployment, documentation |
| LangChain system audit | Diagnose and improve an existing underperforming LangChain system |
| Hourly consulting | Architecture review, prompt engineering strategy |
RAG and retrieval-grounded systems are core to several delivered projects, including the AI Customer Feedback Classification system and AI Video Summarizer. Full case studies at shreyans.tech/ai-case-studies.
FAQ
