Job Description
We are looking for a visionary Senior AI/ML Engineer to define the landscape of artificial intelligence for the 2026 era. At Nexus Future Tech, we are building the infrastructure for tomorrow's digital world, and we need a technical expert who can bridge the gap between theoretical research and production-grade deployment. You will be instrumental in developing scalable, state-of-the-art machine learning models that push the boundaries of what is possible.
In this role, you will work in a high-performance environment that values innovation, ethical AI, and technical excellence. You will have the opportunity to lead architectural decisions, mentor a team of talented engineers, and directly impact the future of our products.
In this role, you will work in a high-performance environment that values innovation, ethical AI, and technical excellence. You will have the opportunity to lead architectural decisions, mentor a team of talented engineers, and directly impact the future of our products.
Responsibilities
- Design and implement cutting-edge neural network architectures for Large Language Models (LLMs) and generative AI applications.
- Optimize deep learning models for high throughput and low latency in production environments.
- Build and maintain robust MLOps pipelines for model training, evaluation, and deployment.
- Collaborate with product managers and engineers to translate complex business requirements into technical solutions.
- Conduct research on emerging AI trends to keep our technology stack at the forefront of the industry.
- Ensure data privacy, security, and ethical compliance in all AI implementations.
- Mentor junior data scientists and engineers, fostering a culture of continuous learning.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in machine learning engineering or applied research.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Deep understanding of distributed systems, data structures, and algorithms.
- Proven track record of deploying scalable machine learning systems in production.