Job Description
We are building the intelligence layer for the autonomous web. Nexus Horizons is seeking a visionary Senior AI Engineer to spearhead the development of Agentic AI systems for the 2026 era. If you are passionate about pushing the boundaries of Large Language Models (LLMs), multi-agent workflows, and autonomous decision-making, this is your chance to lead high-impact innovation in a dynamic, elite tech environment.
As a key member of our AI R&D team, you will architect the next generation of autonomous agents capable of complex reasoning, tool usage, and self-improvement. Join us in shaping the future of human-computer interaction.
As a key member of our AI R&D team, you will architect the next generation of autonomous agents capable of complex reasoning, tool usage, and self-improvement. Join us in shaping the future of human-computer interaction.
Responsibilities
- Architect Autonomous Agents: Design and implement scalable multi-agent systems that can plan, execute, and learn from complex tasks independently.
- Optimize LLM Inference: Engineer high-performance models utilizing quantization, pruning, and caching strategies to reduce latency and operational costs.
- Tool Integration: Develop robust APIs and middleware to enable AI agents to securely interact with external databases, cloud services, and enterprise tools.
- Research & Prototyping: Experiment with cutting-edge research papers in reasoning, memory management, and reinforcement learning to integrate breakthroughs into production.
- Performance Tuning: Monitor, evaluate, and improve the accuracy and reliability of agent outputs across diverse datasets and edge cases.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related quantitative field (or equivalent practical experience).
- Technical Stack: Deep expertise in Python, PyTorch, or TensorFlow. Strong experience with LangChain, LlamaIndex, or similar agent frameworks.
- LLM Mastery: Proven track record of working with state-of-the-art foundation models (GPT-4, Claude, Llama 3) and fine-tuning methodologies (LoRA, PEFT).
- Data Engineering: Experience with vector databases (Pinecone, Milvus, Weaviate) and building Retrieval-Augmented Generation (RAG) pipelines.
- Problem Solving: Exceptional ability to debug complex distributed systems and handle edge cases in AI decision-making logic.