Job Description
Are you ready to define the future of artificial intelligence?
Nexus Horizon Labs is at the forefront of technological evolution, building the infrastructure for the year 2026. We are seeking a visionary AI Lead Engineer to architect scalable, quantum-ready machine learning systems that will power the next generation of intelligent applications.
In this role, you won't just write code; you will shape the ethical and technical landscape of AI. You will lead a high-performance team in developing generative models, optimizing neural networks for real-time processing, and ensuring our systems are resilient against the complexities of a hyper-connected world.
Why Join Nexus Horizon Labs?
- Impact: Work on projects that redefine human-computer interaction.
- Autonomy: High level of ownership over technical strategy and architectural decisions.
- Environment: Collaborative, diverse, and inclusive culture focused on innovation.
Responsibilities
- Lead the end-to-end design and implementation of advanced AI and machine learning pipelines, ensuring they are scalable for future demands.
- Architect robust systems capable of handling massive data throughput, specifically focusing on generative AI and large language models (LLMs).
- Mentor and manage a team of data scientists and engineers, fostering a culture of continuous learning and technical excellence.
- Define and communicate the technical vision for the company’s roadmap leading up to 2026, identifying emerging technologies and integration opportunities.
- Collaborate with cross-functional teams including product managers, security experts, and UX designers to translate complex data into user-centric solutions.
- Ensure all AI deployments adhere to strict ethical guidelines, data privacy regulations, and industry best practices.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related field (PhD preferred).
- Minimum of 7 years of professional experience in software engineering, with at least 4 years in a senior or lead capacity within the AI/ML domain.
- Deep expertise in Python, TensorFlow, PyTorch, and modern MLOps tools.
- Proven track record of deploying production-grade machine learning models at scale.
- Strong understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Experience with ethical AI frameworks and bias mitigation in machine learning models.
- Excellent communication skills, with the ability to translate complex technical concepts to non-technical stakeholders.