Job Description
We are building the systems that will define the technological landscape of 2026 and beyond. As an AI/ML Engineer at Nexus Future Systems, you won't just write code; you will architect the neural pathways of the next generation of intelligence.
In this role, you will work at the intersection of Generative AI, Large Language Models (LLMs), and Ethical AI alignment. We are looking for a visionary engineer who is obsessed with scalability, safety, and the transformative power of AI.
Join a world-class team dedicated to pushing the boundaries of what is possible, ensuring our AI solutions are robust, transparent, and ready for the future.
Responsibilities
- Architect Scalable AI Systems: Design and implement robust machine learning infrastructure capable of handling petabyte-scale data and high-throughput inference.
- Model Fine-Tuning & Optimization: Lead the fine-tuning of state-of-the-art LLMs (e.g., GPT-4, Claude) using custom datasets to enhance domain-specific accuracy.
- AI Safety & Alignment: Develop and implement guardrails and safety protocols to ensure AI outputs adhere to ethical guidelines and safety standards.
- MLOps & Deployment: Streamline the CI/CD pipeline for machine learning models, ensuring rapid, reliable, and automated deployment to production environments.
- Research & Innovation: Stay ahead of 2026 tech trends, researching and prototyping new algorithms that solve complex, unsolved problems in NLP and Computer Vision.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- Experience: 5+ years of professional experience in software engineering with a strong focus on Machine Learning and Deep Learning.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with cloud platforms (AWS, GCP, or Azure).
- LLM Knowledge: Deep understanding of Transformer architectures, RAG (Retrieval-Augmented Generation), and prompt engineering.
- Problem Solving: Ability to debug complex distributed systems and optimize model latency and memory usage.