Website:
toadsters.com
Job details:
We are seeking a passionate and skilled GenAI Engineer to join our team and work on cutting-edge Generative AI applications. The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI frameworks. This role offers an excellent opportunity to build intelligent, scalable AI solutions and grow in the rapidly evolving field of Generative AI. You will be responsible for designing and implementing RAG pipelines, integrating LLMs into production systems, and optimizing AI-powered applications for real-world use cases.
Requirements
- Experience Level: 1-3 years.
- Hands-on experience with LLMs such as OpenAI GPT, Claude, Llama, or Mistral.
- Strong experience building end-to-end RAG pipelines (data ingestion, chunking, embeddings, retrieval, response generation).
- Expertise in LangChain / LangGraph for AI workflows, agents, and orchestration.
- Experience with vector databases (Pinecone, Qdrant, Weaviate, ChromaDB, FAISS).
- Strong prompt engineering and optimization techniques.
- Proficiency in Python with backend development using FastAPI/Django.
- Experience integrating LLM APIs into scalable applications.
- Understanding ofembeddings, NLP concepts, semantic search, and data preprocessing.
- Experience working with SQL (PostgreSQL/MySQL) and NoSQL (MongoDB) databases.
- MLOps, aws sagemaker, redis, DBMS, distributed systems, microservices.
- RAG, graph rag, LangGraph, and vector databases like neo4j, milvus, qdrant.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.
- Relevant certifications in AI/ML or LLM technologies are a plus.
Good To Have
- Experience with model fine-tuning and training custom models.
- Knowledge of LangSmith or other LLM observability tools.
- Understanding of token optimization and cost management for LLM applications.
- Experience with streaming responses and async processing.
- Familiarity with Docker and containerization.
- Exposure to cloud platforms (AWS, Azure, or GCP) for AI deployment.
- Experience with CI/CD pipelines.
- Knowledge of MLOps practices and model versioning.
- Understanding of AI safety, ethics, and responsible AI principles.
- Experience building conversational AI agents or chatbots.
- Frontend integration experience (React, Vue, or Angular).
This job was posted by Gauri Dhyani from Toadster Technologies.
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