Job Description
Welcome to Nexus Future Tech, where innovation meets implementation. We are a cutting-edge technology firm pioneering the next generation of Artificial Intelligence solutions. We are seeking a highly skilled and visionary Senior AI Engineer to join our elite engineering team in San Francisco. If you are passionate about building scalable, robust, and intelligent systems that redefine the boundaries of what is possible, we want to hear from you.
In this role, you will be at the forefront of developing state-of-the-art machine learning models and deploying them into production environments. You will collaborate with cross-functional teams of data scientists, researchers, and product managers to drive the adoption of AI across our product suite. We offer a competitive compensation package, comprehensive benefits, and an environment that fosters creativity and technical excellence.
Why Join Us?
- Work with the latest AI/ML frameworks and technologies.
- Competitive salary and equity package.
- Flexible work arrangements and remote-first culture.
- Professional development opportunities and continuous learning budget.
Responsibilities
- Design, develop, and deploy scalable machine learning models and deep learning architectures.
- Collaborate with data engineers to build robust data pipelines and infrastructure for model training and inference.
- Optimize existing models for speed, accuracy, and resource efficiency.
- Conduct rigorous testing and validation of AI systems to ensure reliability and safety standards.
- Stay abreast of the latest research in AI, NLP, and Computer Vision to integrate cutting-edge advancements.
- Mentor junior engineers and provide technical guidance on best practices in software engineering and data science.
Qualifications
- Master’s or Ph.D. degree in Computer Science, Machine Learning, or a related technical field.
- Minimum of 5+ years of professional experience in AI/ML engineering roles.
- Strong proficiency in Python and frameworks such as TensorFlow, PyTorch, or Keras.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Deep understanding of MLOps practices, model versioning, and CI/CD pipelines.
- Proven track record of deploying production-grade AI solutions.