5 Sb3 Jobs

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI, Reinforcement Learning, Retrieval-Augmented Generation (RAG), and Agentic AI principles to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation, decision-making, and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent coordination . Apply Agentic AI approaches to enable adaptive decision-making, dynamic planning, and autonomous tool usage. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT Agentic AI Concepts: reasoning loops, dynamic tool selection, adaptive workflows LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere RAG & Vector Search: Pinecone, FAISS, ChromaDB, Weaviate for context-aware generation Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI, Reinforcement Learning, Retrieval-Augmented Generation (RAG), and Agentic AI principles to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation, decision-making, and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent coordination . Apply Agentic AI approaches to enable adaptive decision-making, dynamic planning, and autonomous tool usage. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT Agentic AI Concepts: reasoning loops, dynamic tool selection, adaptive workflows LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere RAG & Vector Search: Pinecone, FAISS, ChromaDB, Weaviate for context-aware generation Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.

About the Role: We are seeking a highly motivated and technically proficient AI Agent Development Engineer to join our advanced AI team. In this role, you will design, build, and deploy intelligent, autonomous AI agents that leverage Generative AI , Reinforcement Learning , and Natural Language Processing (NLP) to perform complex, dynamic tasks across diverse domains. You will work at the forefront of AI agent architecture , integrating reasoning, memory, tool usage, and multi-step decision-making capabilities using state-of-the-art libraries and frameworks. Key Responsibilities: Design, develop, and deploy autonomous AI agents for real-world task automation and decision-making and orchestration. Integrate and fine-tune LLMs (Large Language Models) for goal-oriented, tool-using agents. Implement memory-augmented reasoning, retrieval-augmented generation (RAG), and multi-agent coordination. Work with vector databases to store and retrieve contextual memory and knowledge. Optimize agent performance using Reinforcement Learning frameworks and human feedback. Collaborate with cross-functional teams to integrate AI agents into applications and services. Monitor, test, and maintain AI pipelines in production environments. Required Skills & Experience: Strong programming skills in Python (C# or R is a plus). Proven experience in building and deploying AI agents or LLM-based applications . Hands-on expertise in: AI Agent Frameworks: LangChain, AutoGPT, BabyAGI, CrewAI, AgentGPT LLMs & Generative AI: OpenAI (GPT-3.5/4), Hugging Face Transformers, Anthropic Claude, Cohere Vector Search & Memory: Pinecone, FAISS, ChromaDB, Weaviate Reinforcement Learning: OpenAI Gym, Stable-Baselines3, Ray RLlib NLP & Language Tools: spaCy, NLTK, TextBlob Modeling & Deployment: TensorFlow, PyTorch, Keras, Scikit-learn, MLflow APIs & UI Frameworks: Flask, FastAPI, Streamlit, Gradio DevOps: Docker, Git, CI/CD workflows, Kubernetes, Terraform Preferred Qualifications: Experience with agent orchestration tools like LangGraph , ReAct, or Toolformer. Exposure to real-time or asynchronous multi-agent systems. Familiarity with prompt engineering, context management, and RAG pipelines. Contributions to open-source agent/LLM projects or research publications in AI/ML. Why Join Us? Be part of an AI-first product company solving real-world automation and decision-making challenges. Work with a team of AI innovators and contribute to cutting-edge research and applications.