Job Description
Are you ready to define the technological landscape of 2026 and beyond? Nexus Horizon is seeking a visionary Senior AI Engineer to lead our next-generation machine learning initiatives. We are not just building software; we are architecting the future of intelligent systems that will drive industry transformation.
In this role, you will work at the intersection of deep learning, generative AI, and scalable infrastructure. You will have the autonomy to experiment with cutting-edge architectures and the responsibility to deploy robust, production-ready models that solve complex problems.
Why Nexus Horizon?
- Work on projects that define the roadmap for 2026 and beyond.
- Competitive compensation package and equity options.
- Flexible remote-first culture with top-tier benefits.
Responsibilities
- Model Architecture & Development: Design, train, and fine-tune state-of-the-art deep learning models, including Transformers and diffusion models, to solve high-impact business problems.
- System Optimization: Optimize model inference performance for latency and throughput, ensuring seamless integration into our global infrastructure.
- MLOps Pipeline Management: Build and maintain CI/CD pipelines for machine learning, automating model training, validation, and deployment processes using tools like Kubernetes and MLflow.
- Research & Innovation: Stay ahead of the curve in AI research, experimenting with novel architectures and contributing to internal technical blogs and patents.
- Collaboration: Partner with cross-functional teams of data scientists, software engineers, and product managers to translate technical requirements into scalable solutions.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field, with a focus on AI/ML.
- Technical Expertise: Proven experience in Python, PyTorch, TensorFlow, or JAX. Deep understanding of neural network architectures and optimization techniques.
- Experience: Minimum 5+ years of experience in building and deploying ML models in production environments.
- Tools & Cloud: Strong proficiency in cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.