Job Description
Are you ready to define the technological landscape of 2026? At Nexus Horizon Labs, we are pioneering the next generation of autonomous systems and generative AI ecosystems. We are seeking a visionary Senior AI & Machine Learning Architect to lead our Research & Development division. This is not just a job; it is an opportunity to build the foundational models that will drive the industry forward.
We are looking for a technical leader who thrives in ambiguity and possesses the foresight to anticipate the evolving needs of enterprise AI. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and Agentic workflows, we want to hear from you.
Responsibilities
- Architect Scalable ML Pipelines: Design and implement robust, production-grade machine learning architectures capable of handling petabyte-scale data ingestion and real-time inference.
- Lead R&D Strategy: Define and execute the technical roadmap for 2026, focusing on Generative AI, Reinforcement Learning from Human Feedback (RLHF), and multimodal model integration.
- Model Optimization: Spearhead efforts to reduce inference latency and optimize model resource consumption without compromising accuracy or safety.
- Cross-Functional Collaboration: Partner with product managers, data engineers, and security specialists to integrate AI solutions seamlessly into client-facing applications.
- Talent Development: Mentor a high-performing team of data scientists and engineers, fostering a culture of innovation and continuous learning.
- Research Publication: Contribute to academic or industry whitepapers to establish Nexus Horizon Labs as a thought leader in the AI space.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field, with a focus on Artificial Intelligence.
- Experience: 7+ years of professional experience in machine learning, deep learning, or AI engineering, with at least 3 years in a senior or leadership role.
- Technical Proficiency: Deep expertise in Python, PyTorch, TensorFlow, or JAX. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude) and fine-tuning methodologies.
- Infrastructure: Strong background in cloud-native architectures (AWS, GCP, or Azure) and MLOps tools (Kubernetes, Docker, MLflow, Airflow).
- Mathematical Foundation: Solid understanding of linear algebra, calculus, probability, and statistical analysis.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver innovative solutions under tight deadlines.