Job Description
We are at the forefront of the next industrial revolution, and we are seeking a visionary Senior AI Architect to lead the development of our 2026 AI Roadmap. As the landscape of Artificial Intelligence evolves towards agentic workflows and autonomous systems, your expertise will be pivotal in defining the next generation of intelligent software solutions.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering, ensuring our systems are scalable, secure, and ready for the exponential growth of 2026. You will work alongside world-class researchers and engineers to build the future of human-machine interaction.
Why Join Nexus Future Labs?
- Work on cutting-edge projects that define the AI landscape of 2026.
- Competitive compensation package and equity options.
- Flexible remote-first culture with premium benefits.
- Access to state-of-the-art computing infrastructure.
Responsibilities
- Architect and deploy scalable Large Language Model (LLM) pipelines optimized for 2026 performance standards.
- Lead the research and implementation of AI Agents capable of autonomous decision-making and complex task execution.
- Collaborate with product teams to translate 2026 technical roadmaps into actionable engineering specifications.
- Implement robust MLOps strategies to ensure model reliability, monitoring, and continuous learning.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Conduct code reviews and drive architectural best practices across the engineering department.
Qualifications
- 10+ years of experience in software engineering, with at least 5 years specializing in Machine Learning and Artificial Intelligence.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of designing and deploying LLMs (e.g., GPT, Llama, Claude architectures) in production environments.
- Strong understanding of MLOps, DevOps, and cloud infrastructure (AWS, GCP, or Azure).
- Experience with Reinforcement Learning from Human Feedback (RLHF) and fine-tuning techniques.
- Master’s or PhD in Computer Science, Mathematics, or a related field is highly preferred.