AI Agentic Developer

The Role As an Agentic AI Developer at Swaran Soft, you will design, build, and deploy intelligent agent systems that automate complex enterprise workflows. You will work across the full stack of agentic AI development from data acquisition through web scraping and crawling, to agent orchestration using LangChain and LangGraph, to deployment on enterprise platforms via N8N and Supabase. You will not be maintaining legacy systems or building internal tools. You will be building client-facing AI systems that handle real enterprise processes customer support automation, multilingual voice agents, intelligent ticketing, sentiment-driven escalation, and data extraction pipelines. Your work will be seen, used, and evaluated by enterprise stakeholders from day one. What You Will Build & Own Agentic AI Systems & Orchestration Design and build multi-agent AI systems using LangChain and LangGraph including agent memory, tool use, planning loops, and inter-agent communication Develop and deploy intelligent workflow automations using N8N covering enterprise integrations across WhatsApp, Microsoft Teams, CRM systems (Zoho, Freshdesk, ServiceNow), and telephony APIs (Exotel, Twilio) Build conversational AI agents and chatbots with context retention, intent recognition, and multi-turn dialogue management Implement sentiment analysis, intent detection, and tone classification pipelines for voice and text data using LLM-native NLP Create human-in-the-loop escalation systems agents that detect resolution limits and hand off to human operators with context summaries Data Acquisition & Extraction Pipelines Build robust web scraping and crawling systems to acquire structured and unstructured data from enterprise and public web sources Develop data extraction pipelines that clean, normalise, and route scraped data into vector databases and Supabase backends Design and implement RAG (Retrieval-Augmented Generation) pipelines connecting vector databases to LLMs for grounded, knowledge-accurate agent responses Build and maintain vector database schemas (Pinecone, Weaviate, pgvector on Supabase) for semantic search and retrieval at enterprise scale Ensure data pipeline reliability, rate-limit handling, error recovery, and structured logging across all scraping and extraction workloads LLM Engineering & Fine-Tuning Fine-tune open-source LLMs (Mistral, LLaMA, or equivalent) on client-specific datasets for domain-adapted performance Evaluate and select appropriate LLMs for each client use case balancing cost, latency, language support, and accuracy requirements Integrate and route between hosted LLMs (OpenAI, Mistral via OpenRouter) and locally deployed models via Ollama Implement and optimise prompt engineering strategies chain-of-thought, few-shot, system prompts for production agent systems Work with Indian sovereign AI models (Sarvam AI, BharatGen, Krutrim) for multilingual Indian language deployments Backend, Infrastructure & Integration Build and manage Supabase backends including real-time PostgreSQL schemas, Row-Level Security policies, edge functions, and authentication Design API layers that connect agentic AI systems to enterprise CRM, ITSM, messaging, and telephony platforms Deploy and manage AI systems in Docker and Kubernetes environments for cloud, hybrid cloud, and on-premise enterprise clients Maintain RBAC and user management configurations ensuring enterprise-grade access control on all deployed systems Write clean, documented, and testable Python code following production engineering standards, not research or notebook conventions Skills & Experience Must-Have You Will Be Assessed on These 2 4 years of hands-on Python development production-quality code, not just scripts or Jupyter notebooks Demonstrated experience building agentic AI systems using LangChain agents with tool use, memory, and multi-step reasoning Working knowledge of LangGraph for stateful, graph-based agent orchestration you have built at least one production or near-production workflow Experience with web scraping and crawling frameworks BeautifulSoup, Scrapy, Playwright, Selenium, or equivalent including handling anti-scraping measures, pagination, and dynamic content Hands-on experience with Supabase schema design, RLS policies, Postgres functions, and real-time subscriptions Experience with vector databases pgvector, Pinecone, Weaviate, Chroma, or equivalent for semantic search and RAG implementation Experience building chatbots or conversational agents with multi-turn context management and intent handling Working knowledge of N8N for workflow automation you have built at least one multi-step automation with API integrations Understanding of LLM fundamentals tokenisation, context windows, embedding models, prompt engineering, and inference trade-offs Experience with sentiment analysis pipelines either using pre-trained models or LLM-based classification Strong Advantage Sets You Apart Hands-on experience with LLM fine-tuning LoRA, QLoRA, or full fine-tuning on open-source models (Mistral, LLaMA, Phi) Experience integrating voice AI stacks STT (Whisper) and TTS (ElevenLabs, PlayHT) in production agent systems Exposure to Indian language NLP models supporting Hindi, Tamil, Telugu, or other Indian languages Experience with enterprise API integrations WhatsApp Cloud API, Microsoft Teams Bot Framework, Zoho, Freshdesk, Exotel, or Twilio Familiarity with Docker and Kubernetes for deploying AI applications in cloud or on-premise environments Contributions to open-source AI projects, published technical writing, or demonstrated personal AI projects on GitHub