Job Summary :
We are seeking a hands-on Gen AI Architect with strong Python skills, proven experience in Agentic AI solutions, and exposure to React fullstack development.
The ideal candidate will architect and deliver intelligent, context-aware systems using LLMs, RAG, and agent frameworks while collaborating with front-end teams to deliver seamless user Responsibilities :
- Architect and build agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom agent orchestration pipelines.
- Design and implement end-to-end Gen AI solutions including document Q&A, summarization, chat interfaces, and autonomous workflows.
- Develop robust and reusable Python modules for LLMs, prompt chaining, embeddings, and vector stores (e.g., FAISS, Weaviate, Pinecone).
- Integrate Gen AI pipelines into fullstack applications with React.js frontends and REST / GraphQL backends.
- Guide the implementation of RAG (Retrieval-Augmented Generation) solutions using chunking, metadata management, and prompt optimization.
- Collaborate with UI / UX and frontend developers to deliver interactive AI tools and dashboards.
- Drive adoption of cloud-native AI services on Azure, AWS, or GCP and containerized deployment using Docker / Kubernetes.
- Evaluate latest LLM advancements (GPT-4, Claude, Mistral, LLaMA, Gemini, etc.) and recommend adoption Skills & Qualifications :
- Bachelor's or Masters degree in Computer Science, Engineering, or equivalent.
- 10+ years of overall experience, with 3+ years in Gen AI / LLM-based solutions.
- Strong proficiency in Python and experience with LangChain, Transformers, OpenAI SDK, and other Gen AI libraries.
- Hands-on experience building agentic workflows with autonomous or semi-autonomous agents.
- Working knowledge of React.js, Node.js, RESTful APIs, and fullstack integration best practices.
- Experience with vector databases, prompt engineering, tokenization, and embeddings.
- Familiarity with DevOps, CI / CD for ML pipelines, and scalable deployment Skills :
- Experience in enterprise AI applications across domains like BFSI, Healthcare, or Retail.
- Exposure to Power BI, Snowflake, or Microsoft Fabric ecosystems.
- Knowledge of MLOps, logging, observability, and secure API design.
- Open-source contributions or AI research background is a plus.
(ref : hirist.tech)