1 to 25 of 29 Faiss Jobs

RAG (Retrieval-Augmented Generation), Graph RAG, Agentic RAG, and multi-agent systems. Vector Databases & Embeddings: Expertise in working with various embedding models and vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS). Advanced AI Concepts: Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, autonomous agent design, and evaluation methodologies for complex AI systems. More ❯

scaling multi-agent systems or tool-augmented LLM workflows. Familiarity with secure cloud development practices and IAM role design. Understanding of LLM fine-tuning, embeddings, vector stores (e.g., Pinecone, FAISS, OpenSearch). Exposure to contact centre automation, conversational agents, or RAG pipelines. Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible More ❯

NumPy Expertise in Large Language Models (LLMs) including fine-tuning, prompt engineering, and working daily with models like GPT, Claude, Gemini Hands-on experience with vector databases (e.g., OpenSearch, FAISS) for efficient similarity search and semantic search & retrieval of text embeddings Skilled in implementing conversational AI systems leveraging APIs like OpenAI Chat Completion and Assistant endpoints AWS, Terraform, GitHub Actions More ❯

scaling multi-agent systems or tool-augmented LLM workflows. · Familiarity with secure cloud development practices and IAM role design. · Understanding of LLM fine-tuning, embeddings, vector stores (e.g., Pinecone, FAISS, OpenSearch). · Exposure to contact centre automation, conversational agents, or RAG pipelines. Please click here to find out more about our Key Information Documents. Please note that the documents provided More ❯

alignment with governance and compliance frameworks. Proficiency in Pythonand tools such as LangChain, Pandas, PyTorch/TensorFlow, and FastAPI. Strong understanding of prompt engineering, RAG pipelines, vector databases (e.g., FAISS, Chroma), and LLM evaluation strategies. Familiarity with software engineering best practices, including: REST API design and integration Git version control CI/CD pipelines Cloud environments(e.g., AWS, Azure, GCP More ❯

services. Strong skills in MLOps: containerisation (Docker, Kubernetes), cloud deployment (AWS, GCP, Azure), and CI/CD pipelines. Experience with prompt engineering, LLM evaluation, and vector databases (Pinecone, Weaviate, FAISS). Excellent communication skills and cross-functional collaboration experience. Benefits: Our team at OneClickComply is central to our 100% customer satisfaction, and we offer some of the best benefits packages More ❯

and Qualifications Skills & Expertise Strong experience in machine learning, deep learning, and statistical analysis. Expertise in Python, with proficiency in ML and NLP libraries such as Scikit-learn, TensorFlow, Faiss, LangChain, Transformers and PyTorch. Experience with big data tools such as Hadoop, Spark, and Hive. Familiarity with CI/CD and MLOps frameworks for building end-to-end ML pipelines. More ❯

development. Proficiency in Python, ML frameworks (PyTorch, TensorFlow), and cloud-native AI solutions. Hands-on experience with Azure Cognitive Services, OpenAI APIs, and vector search (e.g., Azure AI Search, FAISS, CosmosDB, Pinecone, etc.). Experience in AI security, MLOps, and deploying scalable AI solutions. Ability to troubleshoot and optimize AI models for performance and accuracy About Capgemini Capgemini is a More ❯

use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term agent behaviours. Deploy models and orchestrate AI More ❯

use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term agent behaviours. Deploy models and orchestrate AI More ❯

with memory frameworks like Mem0 or Letta. Understanding and production experience with traditional RAG and agentic RAG architectures. Strong grasp of embedding systems and vector databases (e.g., Pinecone, Weaviate, FAISS). Production experience with LiveKit or similar audio/video platforms. What You’ll Be Doing Building and deploying scalable AI features, agents, and services powered by LLMs. Prototyping and More ❯

finetuning largescale transformer models (BERT, GPT) and promptengineering for sentiment tasks Background building activelearning and annotation pipelines to bootstrap training data Familiarity with semantic search or vector databases (Elasticsearch, FAISS, Pinecone) for topic modeling and similarity queries Familiarity with crypto markets, order books, and risk-management frameworks Familiarity with anomalydetection methods for streaming text and timeseries data Experience developing EVM More ❯

Azure Data Factory, Azure Synapse, and Azure Functions. Implementing modern retrieval techniques such as vector search, semantic search and Retrieval-Augmented Generation (RAG) using tools like Azure Cognitive Search, FAISS, Pinecone, or Weaviate. Familiarity with data governance, privacy, and ethical AI principles. Experience with DevOps tools and practices, including CI/CD pipelines, infrastructure as code and monitoring. Applied knowledge More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to adeeb.rahman@opusrs.com More ❯

large language models and enthusiasm for solving real-world product challenges Clear communication and ability to thrive in a distributed team Nice to Have: Experience with vector databases (e.g., FAISS, Weaviate, pgvector) Knowledge of survey data structures or market research workflows Familiarity with statistics (e.g., weighting, significance testing) Experience with Docker, Hugging Face Transformers, or cloud-based deployment Awareness of More ❯

refining prompts for LLMs (OpenAI, Claude, etc.) Building prompt libraries and evaluation frameworks Structuring unstructured data (PDFs, notes, forms) into usable formats Working with RAG pipelines and vector databases (FAISS, Pinecone, etc.) Embedding LLMs into user-facing healthcare tools (e.g., AI assistants, in-context help) Collaborating with product, design, and clinical teams to ship real features What You’ll Bring More ❯