LlamaIndex Experience with Docker and Kubernetes Fluent written and verbal communication skills in English Working knowledge of some Vector Databases such as OpenSearch, Qdrant, Weaviate, LanceDB, etc. - an advantage Working knowledge with Snowflake, BigQuery, and/or Databricks - an advantage GCP or Azure knowledge (DevOps/MLOps) - an advantage ML More ❯
and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or More ❯
and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or More ❯
retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely More ❯
retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely More ❯
retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely More ❯
NLP, and conversational AI (e.g., GPT, Claude, Mistral, LLaMA, etc.) Expertise with frameworks like LangChain, Hugging Face, OpenAI, RAG pipelines, and vector databases (e.g., Weaviate, Pinecone, Chroma) Solid knowledge of AI system architecture, including model serving, monitoring, and optimization Strong programming skills in Python, with experience building APIs and integrating More ❯
and other competitions are preferred. Strong analytical and statistical modeling skills. Experience with machine learning (Generative AI) frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Langchain, Weaviate, Langgraph, LlamaIndex). Proven track record of applying data science to solve real-world problems. Excellent communication and collaboration skills. Why AI71: Proven performance of More ❯
IOI, Top Coder, Kaggle). Technical Skills : Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Generative AI technologies, and libraries like Langchain, Weaviate, Langgraph, LlamaIndex. Track Record : Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems. Collaboration : Excellent communication and collaboration More ❯
IOI, Top Coder, Kaggle). Technical Skills : Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Generative AI technologies, and libraries like Langchain, Weaviate, Langgraph, LlamaIndex. Track Record : Demonstrated success in applying data science and machine learning to solve real-world, business-critical problems. Collaboration : Excellent communication and collaboration More ❯
Deep knowledge of cloud infrastructure and services. Experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers). Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS). Experience with RAG (Retrieval-Augmented Generation), LLMOps, or LangChain/LlamaIndex. Proficiency with Infra-as-Code tools like Terraform. Understanding of data pipelines More ❯
Deep knowledge of cloud infrastructure and services. Experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers). Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS). Experience with RAG (Retrieval-Augmented Generation), LLMOps, or LangChain/LlamaIndex. Proficiency with Infra-as-Code tools like Terraform. Understanding of data pipelines More ❯
nice if you have: Hands-on experience with OpenAI's GPT-4o, o1, and Claude models from Anthropic Familiarity with vector databases (e.g., Pinecone, Weaviate, or similar) Experience building applications with Docker and Kubernetes Proven expertise in building highly secure, fault-tolerant APIs Experience building high-performance, distributed systems at More ❯
solving skills and ability to work in a fast-paced, agile environment. Nice-to-Have Skills: Hands-on experience with Vector Databases (FAISS, Pinecone, Weaviate, PGVector, etc.). Experience fine-tuning LLMs for domain-specific applications. Knowledge of data privacy, governance, and compliance in AI-driven systems. Previous work in More ❯
chatbots. Hands-on use of agent/RAG orchestration frameworks (LangChain, LangGraph, CrewAI, Autogen ). Working knowledge of vector stores (e.g. pgvector, Pinecone, OpenSearch, Weaviate ). Professional English and a product mindset focused on launching, measuring and iterating. Nice to have Fine-tuning or RLHF/DPO on open-source More ❯
extending an *AWS stack (Terraform, ECS Fargate, ALB, Secrets Manager, KMS)*. • Hands-on with *LLM APIs* and at least one *vector database* (Pinecone, Weaviate, OpenSearch, etc.). • Multi-tenant data design with GDPR awareness. • CI/CD and automated-testing mindset. Nice-to-Haves • *Solution-architect background*—ability to More ❯
strategies using vector/graph database technologies. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Experience with testing methodologies (unit/functional/e2e) and tools (unittest, pytest, etc.). Experience with agile development methodologies and version control … and experience in API design and implementation. Experience in developing ingestion workflows/pipelines for vector indexes using well-known providers (e.g., FAISS, Pinecone, Weaviate, Chroma). Build and deploy robust AI applications using technologies such as Llamanindex and LangChain for seamless orchestration of LLM (large language model) pipelines. Design More ❯
we use it to power our analytics) OCR engines (we use AWS Textract, GDocAI, and we have used tesseractOCR in the past) Prompt Engineering Weaviate (we use it for RAG in LLM powered tasks and for hybrid searches) Kubernetes (we run Weaviate and other specific services on Kubernetes) CircleCI DataDog More ❯
