Data Scientist
Job Title: Data scientist with Gen AI and Agentic AI
Location: Remote
Work Type: Contract
About the Role:
We are seeking a highly skilled Generative AI Engineer with expertise in Agentic AI to design, develop, and deploy intelligent AI agents capable of autonomous decision-making, reasoning, and adaptation. You will work on cutting-edge AI solutions that integrate LLMs, reinforcement learning, prompt engineering, and multi-agent frameworks to create scalable and efficient AI-driven systems.
Key Responsibilities:
• Design and implement Agentic AI architectures using LLMs (GPT-4, Claude, Gemini, etc.) and multi-agent collaboration frameworks.
• Develop autonomous AI agents that can reason, plan, and take actions in dynamic environments.
• Work with Reinforcement Learning (RL), Fine-tuning, and Retrieval-Augmented Generation (RAG) to improve agent efficiency.
• Optimize prompt engineering and context management for LLM-driven agents.
• Build scalable APIs and pipelines for integrating AI agents into real-world applications.
• Collaborate with cross-functional teams including ML engineers, software developers, and product managers.
• Stay updated on state-of-the-art research in generative AI, multi-agent systems, and AI autonomy.
Required Skills & Qualifications:
• Bachelor's/Master's/PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Experience in AI/ML engineering, with a focus on generative AI and autonomous agents.
• Strong expertise in Python, PyTorch, TensorFlow, LangChain, and Auto-GPT.
• Hands-on experience with LLMs (OpenAI, Anthropic, Cohere, Mistral, etc.) and fine-tuning techniques.
• Proficiency in Reinforcement Learning, Multi-Agent Systems, and Prompt Engineering.
• Experience working with vector databases (FAISS, Pinecone, Weaviate) and APIs.
• Strong problem-solving skills and the ability to work in a fast-paced environment.
Nice to Have:
• Experience with symbolic reasoning, knowledge graphs, or neurosymbolic AI.
• Knowledge of MLOps, AI ethics, and responsible AI principles.
• Background in game theory, planning algorithms, or cognitive architectures.
Job Title: Data scientist with Gen AI and Agentic AI
Location: Remote
Work Type: Contract
About the Role:
We are seeking a highly skilled Generative AI Engineer with expertise in Agentic AI to design, develop, and deploy intelligent AI agents capable of autonomous decision-making, reasoning, and adaptation. You will work on cutting-edge AI solutions that integrate LLMs, reinforcement learning, prompt engineering, and multi-agent frameworks to create scalable and efficient AI-driven systems.
Key Responsibilities:
• Design and implement Agentic AI architectures using LLMs (GPT-4, Claude, Gemini, etc.) and multi-agent collaboration frameworks.
• Develop autonomous AI agents that can reason, plan, and take actions in dynamic environments.
• Work with Reinforcement Learning (RL), Fine-tuning, and Retrieval-Augmented Generation (RAG) to improve agent efficiency.
• Optimize prompt engineering and context management for LLM-driven agents.
• Build scalable APIs and pipelines for integrating AI agents into real-world applications.
• Collaborate with cross-functional teams including ML engineers, software developers, and product managers.
• Stay updated on state-of-the-art research in generative AI, multi-agent systems, and AI autonomy.
Required Skills & Qualifications:
• Bachelor's/Master's/PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Experience in AI/ML engineering, with a focus on generative AI and autonomous agents.
• Strong expertise in Python, PyTorch, TensorFlow, LangChain, and Auto-GPT.
• Hands-on experience with LLMs (OpenAI, Anthropic, Cohere, Mistral, etc.) and fine-tuning techniques.
• Proficiency in Reinforcement Learning, Multi-Agent Systems, and Prompt Engineering.
• Experience working with vector databases (FAISS, Pinecone, Weaviate) and APIs.
• Strong problem-solving skills and the ability to work in a fast-paced environment.
Nice to Have:
• Experience with symbolic reasoning, knowledge graphs, or neurosymbolic AI.
• Knowledge of MLOps, AI ethics, and responsible AI principles.
• Background in game theory, planning algorithms, or cognitive architectures.
