Job Description
We are on the forefront of defining the technological landscape for 2026. Nexus Future Systems is seeking a visionary Senior AI/ML Engineer to lead the development of our next-generation generative intelligence platform. In this role, you won't just build models; you will architect the cognitive infrastructure that will define the future of human-machine interaction. If you are passionate about pushing the boundaries of Large Language Models (LLMs), autonomous agents, and ethical AI deployment, we want to meet you.
Why Join Us?
- 2026 Vision: Work on cutting-edge technology specifically designed to be enterprise-ready by 2026.
- Global Impact: Your code will power the next evolution of productivity for Fortune 500 companies.
- Top-Tier Team: Collaborate with PhDs and industry veterans in a state-of-the-art facility.
Responsibilities
- Architect and implement scalable machine learning pipelines for generative AI models, specifically targeting 2026 scalability requirements.
- Research and optimize state-of-the-art transformer architectures and fine-tune LLMs for specific enterprise use cases.
- Collaborate with cross-functional teams (Product, Engineering, Ethics) to ensure AI deployment aligns with safety and compliance standards.
- Debug complex model failures and improve inference latency and throughput in production environments.
- Contribute to the open-source community and publish research on novel AI methodologies.
- Define technical roadmaps for the AI department, identifying key innovations needed for the 2026 transition.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in building and deploying production-grade machine learning models.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of Natural Language Processing (NLP) and Transformer models (BERT, GPT, T5).
- Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, Azure).
- Strong grasp of AI ethics, bias mitigation, and responsible AI governance.