Job Description
We are entering the era of autonomous intelligence. At Nexus Future Systems, we are not just building AI; we are architecting the autonomous workforce that will define 2026 and beyond. If you are a visionary engineer passionate about creating self-sustaining, intelligent agents capable of complex problem-solving, we want you on our team.
In this role, you will be at the forefront of the Agentic AI revolution. You will design, train, and deploy next-generation LLM-powered agents that can reason, plan, and execute tasks with minimal human intervention. You will work with a world-class team of researchers and engineers to push the boundaries of what is possible in generative AI and autonomous systems.
Why Join Us?
- Impact: Shape the future of work with technology that actually works.
- Innovation: Work with state-of-the-art models and cutting-edge architectures.
- Equity: Competitive compensation package with significant equity upside.
Responsibilities
- Architect Autonomous Systems: Design and implement complex multi-agent architectures that leverage LLMs for autonomous task execution and decision-making.
- Model Optimization: Fine-tune and optimize foundation models for specific agentic workflows to ensure high efficiency and accuracy.
- Tool Integration: Build robust bridges between AI agents and external tools, APIs, and databases to enable real-world application.
- RAG & Memory Systems: Develop advanced Retrieval-Augmented Generation systems and episodic memory mechanisms to give agents long-term context.
- Security & Safety: Implement guardrails and safety protocols to ensure AI agent behavior remains aligned and secure.
- Research & Development: Stay ahead of the curve by researching new techniques in chain-of-thought reasoning and agent orchestration.
Qualifications
- Education: Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, or a related field.
- Experience: 5+ years of experience in software engineering or machine learning engineering.
- Core Tech: Deep proficiency in Python, PyTorch, or TensorFlow.
- AI Expertise: Strong experience with LLMs (GPT-4, Claude, Llama), RAG pipelines, and LangChain/LlamaIndex.
- System Design: Demonstrated ability to design scalable, distributed systems for AI applications.
- Problem Solving: Experience in debugging complex, non-deterministic AI systems.