Job Description
We are Nexus Horizon, a pioneering force in next-generation artificial intelligence. We are seeking a visionary Senior AI Engineer to lead the development of scalable machine learning systems that define the future of our industry. If you are passionate about pushing the boundaries of Generative AI and Large Language Models (LLMs), this is your opportunity to build the future.
Key Highlights:
- Work on cutting-edge AI infrastructure that scales to millions of users.
- Competitive compensation package including equity and bonuses.
- Collaborate with world-class researchers and engineers in a dynamic tech hub.
Role Overview:
As a Senior AI Engineer, you will own the architecture and deployment of our core AI models. You will bridge the gap between theoretical research and production-grade software, ensuring our systems are robust, efficient, and ethically sound.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art Deep Learning and Generative AI models using Python and PyTorch.
- System Architecture: Design scalable MLOps pipelines to manage the full lifecycle of AI models from experimentation to production deployment.
- Performance Optimization: Optimize inference latency and throughput to ensure real-time performance in high-demand environments.
- Collaboration: Partner with product managers and data scientists to translate complex business requirements into technical AI solutions.
- Technical Leadership: Mentor junior engineers, conduct code reviews, and establish best practices for machine learning engineering within the team.
- Research: Stay ahead of the curve by integrating the latest advancements in AI research into our product stack.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: Minimum of 5 years of professional experience in AI/ML engineering or a similar data science role.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, or JAX. Strong understanding of LLMs, Transformers, and NLP.
- Infrastructure: Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- MLOps: Demonstrated experience in deploying models to production environments and implementing CI/CD for ML workflows.
- Problem Solving: Strong analytical skills with a track record of solving complex technical challenges in high-stakes environments.