Job Description
We are building the infrastructure for the next era of human-machine interaction. Nexus Future Labs is seeking a visionary Senior AI Engineer to lead the development of our proprietary Agentic AI systems designed for the 2026 landscape. If you are passionate about pushing the boundaries of Large Language Models (LLMs), multimodal learning, and autonomous agents, we want to hear from you.
As a key member of our elite engineering team, you will architect scalable AI solutions that redefine efficiency and intelligence in enterprise environments. You will work in a fast-paced, remote-first culture that values innovation, diversity, and high performance.
Responsibilities
- Design, train, and deploy state-of-the-art Large Language Models (LLMs) and generative AI architectures tailored for 2026 use cases.
- Lead the end-to-end machine learning lifecycle, from data engineering and model training to productionization and monitoring.
- Optimize model inference latency and throughput to ensure seamless user experiences at scale.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to define AI product roadmaps.
- Research and implement cutting-edge techniques in prompt engineering, fine-tuning, and reinforcement learning from human feedback (RLHF).
- Establish best practices for MLOps, ensuring robust, reproducible, and secure AI deployments.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on Artificial Intelligence or Deep Learning.
- Minimum of 5+ years of professional experience in machine learning, AI engineering, or a similar technical role.
- Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Extensive experience with transformer architectures, LLMs (GPT, BERT, LLaMA), and RAG (Retrieval-Augmented Generation) systems.
- Demonstrated ability to deploy models to cloud platforms (AWS, GCP, or Azure) using containerization tools like Docker and Kubernetes.
- Proven track record of publishing research or delivering production-level AI products.
- Exceptional problem-solving skills and the ability to translate complex business requirements into technical solutions.