Job Description
We are on the cutting edge of the 2026 technology landscape. At Nexus Future Labs, we are building the cognitive infrastructure for the next generation of intelligent systems. We are seeking a visionary Senior AI/ML Engineer to lead our Generative AI division. You will be responsible for architecting scalable neural networks, fine-tuning large language models (LLMs), and deploying autonomous agents that redefine human-computer interaction.
If you are passionate about the future of AI, possess a deep understanding of deep learning architectures, and want to work on projects that will define the era of 2026, we want to meet you.
Responsibilities
- Architect Next-Gen Models: Design and implement state-of-the-art deep learning architectures, specifically focusing on Transformer models and reinforcement learning for Generative AI.
- Model Optimization: Drive the research and engineering efforts to optimize model inference latency and memory efficiency for real-time edge deployment.
- LLM Fine-Tuning: Spearhead the fine-tuning and alignment of large language models using proprietary datasets to enhance reasoning and safety capabilities.
- Cross-Functional Leadership: Collaborate with product managers, data scientists, and software engineers to translate technical requirements into scalable production solutions.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of innovation, code quality, and continuous learning.
- R&D Strategy: Stay ahead of the curve on emerging AI trends (e.g., Quantum AI integration, Neuromorphic computing) and integrate relevant technologies into our roadmap.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related field with a focus on Artificial Intelligence.
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years specifically in Generative AI or Large Language Models.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed training frameworks (e.g., Ray, Horovod).
- Domain Knowledge: Deep understanding of NLP, computer vision, or multimodal learning paradigms.
- Tools: Experience with MLOps platforms (e.g., Kubeflow, MLflow) and cloud infrastructure (AWS, GCP, or Azure).
- Problem Solving: Strong ability to troubleshoot complex numerical instability issues and optimize GPU resource utilization.