As a Senior LLM Engineer, you will be responsible for designing, building, and optimizing intelligent agents powered by Large Language Models (LLMs). Your primary focus will be on working with cutting-edge AI applications, pre-training LLMs, fine-tuning open-source models, integrating multi-agent systems, and deploying scalable solutions in production environments. Your key responsibilities will include developing and fine-tuning LLM-based models and AI agents for automation, reasoning, and decision-making. You will also be tasked with building multi-agent systems that coordinate tasks efficiently, designing prompt engineering, retrieval-augmented generation (RAG), and memory architectures, as well as optimizing inference performance and reducing hallucinations in LLMs. Additionally, you will be expected to integrate LLMs with APIs, databases, and external tools for real-world applications, implement reinforcement learning with human feedback (RLHF) and continual learning strategies, and collaborate with research and engineering teams to enhance model capabilities. To qualify for this role, you should have at least 5+ years of experience in AI/ML, with a minimum of 2+ years specifically focused on LLMs or AI agents. You should possess strong experience in Python, LangChain, LlamaIndex, Autogen, Hugging Face, etc. Experience with open-source LLMs such as LLaMA, Mistral, Falcon, etc. is also required. Hands-on experience in LLM deployments with strong inference capabilities using robust frameworks like vLLM is essential, as is expertise in building multi-modal RAG systems. Knowledge of vector databases (FAISS, Chroma) for retrieval-based systems, experience with LLM fine-tuning, downscaling, prompt engineering, and model inference optimization are all important qualifications. Familiarity with multi-agent systems, cognitive architectures, or autonomous AI workflows, as well as expertise in cloud platforms (AWS, GCP, Azure) and scalable AI deployments, will be beneficial. Strong problem-solving and debugging skills are also highly valued for this role.,
Role Overview: As a Senior LLM Engineer, your primary responsibility will be to design, build, and optimize intelligent agents powered by Large Language Models (LLMs). You will have the opportunity to work on cutting-edge AI applications, pre-train LLMs, fine-tune open-source models, integrate multi-agent systems, and deploy scalable solutions in production environments. Key Responsibilities: - Develop and fine-tune LLM-based models and AI agents for automation, reasoning, and decision-making. - Build multi-agent systems that coordinate tasks efficiently. - Design prompt engineering, retrieval-augmented generation (RAG), and memory architectures. - Optimize inference performance and reduce hallucinations in LLMs. - Integrate LLMs with APIs, databases, and external tools for real-world applications. - Implement reinforcement learning with human feedback (RLHF) and continual learning strategies. - Collaborate with research and engineering teams to enhance model capabilities. Qualifications Required: - 5+ years in AI/ML, with at least 2+ years in LLMs or AI agents. - Strong experience in Python, LangChain, LlamaIndex, Autogen, Hugging Face, etc. - Experience with open-source LLMs (LLaMA, Mistral, Falcon, etc.). - Hands-on experience in LLM deployments with strong inference capabilities using robust frameworks such as vLLM. - Building multi-modal RAG systems. - Knowledge of vector databases (FAISS, Chroma) for retrieval-based systems. - Experience with LLM fine-tuning, downscaling, prompt engineering, and model inference optimization. - Familiarity with multi-agent systems, cognitive architectures, or autonomous AI workflows. - Expertise in cloud platforms (AWS, GCP, Azure) and scalable AI deployments. - Strong problem-solving and debugging skills. Role Overview: As a Senior LLM Engineer, your primary responsibility will be to design, build, and optimize intelligent agents powered by Large Language Models (LLMs). You will have the opportunity to work on cutting-edge AI applications, pre-train LLMs, fine-tune open-source models, integrate multi-agent systems, and deploy scalable solutions in production environments. Key Responsibilities: - Develop and fine-tune LLM-based models and AI agents for automation, reasoning, and decision-making. - Build multi-agent systems that coordinate tasks efficiently. - Design prompt engineering, retrieval-augmented generation (RAG), and memory architectures. - Optimize inference performance and reduce hallucinations in LLMs. - Integrate LLMs with APIs, databases, and external tools for real-world applications. - Implement reinforcement learning with human feedback (RLHF) and continual learning strategies. - Collaborate with research and engineering teams to enhance model capabilities. Qualifications Required: - 5+ years in AI/ML, with at least 2+ years in LLMs or AI agents. - Strong experience in Python, LangChain, LlamaIndex, Autogen, Hugging Face, etc. - Experience with open-source LLMs (LLaMA, Mistral, Falcon, etc.). - Hands-on experience in LLM deployments with strong inference capabilities using robust frameworks such as vLLM. - Building multi-modal RAG systems. - Knowledge of vector databases (FAISS, Chroma) for retrieval-based systems. - Experience with LLM fine-tuning, downscaling, prompt engineering, and model inference optimization. - Familiarity with multi-agent systems, cognitive architectures, or autonomous AI workflows. - Expertise in cloud platforms (AWS, GCP, Azure) and scalable AI deployments. - Strong problem-solving and debugging skills.