Job Description
Are you ready to shape the technological landscape of 2026 and beyond? Nexus Future Labs is at the forefront of the AI revolution, and we are looking for a visionary Lead AI Engineer (2026 Vision) to drive our next generation of generative intelligence systems.
In this pivotal role, you will not just build models; you will define the architecture that powers the autonomous, multimodal AI experiences of tomorrow. You will collaborate with world-class researchers and engineers to solve complex problems at the intersection of deep learning, natural language processing, and large-scale distributed systems.
If you are passionate about pushing the boundaries of what AI can achieve and want to be part of a team that is preparing for the future, we want to hear from you.
Responsibilities
- Architect Future-Ready AI: Design and implement scalable, robust, and efficient deep learning models, specifically focusing on LLMs and Generative AI architectures suitable for 2026 deployment.
- Optimize Inference: Lead initiatives to reduce latency and computational costs for large-scale AI inference in production environments.
- Research & Innovation: Stay ahead of the curve by evaluating and integrating cutting-edge research (e.g., Mixture of Experts, Reinforcement Learning from Human Feedback) into our product roadmap.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of technical excellence and rapid prototyping.
- System Integration: Collaborate with backend and frontend teams to seamlessly integrate AI models into user-facing products.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years leading AI teams or projects.
- Core Skills: Proficiency in Python, PyTorch, or TensorFlow; deep understanding of neural network architectures.
- NLP Expertise: Strong background in Natural Language Processing, Transformers, and Large Language Models.
- Cloud Native: Experience deploying models on AWS, GCP, or Azure using containerization (Docker/Kubernetes).
- Problem Solving: Ability to troubleshoot complex distributed systems and optimize model performance in real-time.