/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS etc.). Experience in architecting and scaling GenAI chatbots Experience in Agentic AI solutions. Good knowledge of Agentic AI frameworks (LangGraph, AutoGen, CrewAI) and orchestration tooling (MCP Servers) in More ❯
london (city of london), south east england, united kingdom
Infosys
/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS etc.). Experience in architecting and scaling GenAI chatbots Experience in Agentic AI solutions. Good knowledge of Agentic AI frameworks (LangGraph, AutoGen, CrewAI) and orchestration tooling (MCP Servers) in More ❯
/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS etc.). Experience in architecting and scaling GenAI chatbots Experience in Agentic AI solutions. Good knowledge of Agentic AI frameworks (LangGraph, AutoGen, CrewAI) and orchestration tooling (MCP Servers) in More ❯
Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and RAG Strong proficiency in Python and experience with frameworks like PyTorch or TensorFlow. Experience working with vector databases (e.g., FAISS, Weaviate, Pinecone). Familiarity with HR systems and data (Workday, SAP SuccessFactors, etc.) is a plus. Excellent communication skills and ability to translate technical concepts for non-technical stakeholders. Ideally you More ❯
on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years’ experience in full-stack development. Strong background in RAG systems , vector databases (pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React/ More ❯
