Job Description
Are you ready to build the future? Nexus Future Labs is seeking a visionary Senior AI/ML Engineer (2026 Vision) to lead the architectural design for our next-generation artificial intelligence infrastructure. As we prepare for the technological paradigm shift of 2026, you will be at the forefront of integrating Generative AI, Quantum-ready algorithms, and sustainable data centers into our core products. This is a rare opportunity to define the standards for computing in the coming decade.
We are looking for a self-starter who thrives in ambiguity and possesses a deep understanding of Large Language Models (LLMs), MLOps, and scalable distributed systems. You will work closely with our CTO and research scientists to deploy models that are not only powerful but also ethically aligned and energy-efficient.
We are looking for a self-starter who thrives in ambiguity and possesses a deep understanding of Large Language Models (LLMs), MLOps, and scalable distributed systems. You will work closely with our CTO and research scientists to deploy models that are not only powerful but also ethically aligned and energy-efficient.
Responsibilities
- Lead the architectural design and implementation of scalable AI models tailored for the 2026 market landscape.
- Optimize machine learning pipelines for low-latency inference and high throughput in cloud environments.
- Collaborate with cross-functional teams to integrate AI capabilities into consumer and enterprise software.
- Define best practices for MLOps, ensuring reproducibility and version control of all models.
- Conduct rigorous testing and validation of AI systems to ensure reliability and safety standards.
- Research and prototype emerging technologies (e.g., Neuro-Symbolic AI) to stay ahead of the 2026 roadmap.
- Mentor junior engineers and foster a culture of continuous learning and innovation.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 5 years of professional experience in AI/ML engineering, specifically with Python and TensorFlow or PyTorch.
- Proven experience in deploying large-scale machine learning models in production environments.
- Strong understanding of distributed systems, microservices architecture, and cloud platforms (AWS, GCP, or Azure).
- Experience with prompt engineering and fine-tuning LLMs for specific industry use cases.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.