Job Description
Be a Pioneer at Nexus 2026
Welcome to Nexus 2026, where we are defining the technological landscape of the future. We are looking for a visionary Senior AI/ML Engineer to lead the development of next-generation artificial intelligence systems that will power the enterprise of tomorrow.
In this role, you will not just build models; you will architect the intelligence behind our flagship products. You will work in a dynamic, high-performance environment with a team of world-class researchers and engineers. If you are passionate about Generative AI, Large Language Models (LLMs), and ethical AI implementation, we want to hear from you.
Why join Nexus 2026?
- Work on cutting-edge AI research with real-world impact.
- Competitive equity package and performance bonuses.
- Flexible remote-first culture with state-of-the-art equipment.
Responsibilities
- Design, train, and fine-tune large-scale machine learning models and deep neural networks.
- Lead the end-to-end MLOps pipeline, from data ingestion to model deployment and monitoring in production.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to translate business requirements into technical AI solutions.
- Optimize model inference speed and reduce latency for real-time applications.
- Conduct rigorous research to explore novel algorithms and architectures for NLP and Computer Vision tasks.
- Ensure model transparency, fairness, and robustness by implementing explainable AI (XAI) techniques.
- Establish best practices for code quality, documentation, and version control within the AI research group.
Qualifications
- Master’s or Ph.D. in Computer Science, Mathematics, Statistics, or a related field.
- Minimum of 5 years of professional experience in Machine Learning, Deep Learning, or AI research.
- Proficient programming skills in Python, including extensive experience with PyTorch or TensorFlow.
- Strong understanding of statistical methods, optimization algorithms, and distributed computing.
- Experience deploying models on cloud platforms such as AWS, GCP, or Azure using containerization tools like Docker and Kubernetes.
- Proven track record of publishing research papers or delivering high-impact AI products.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.