Job Description
Are you ready to define the technological landscape of 2026? Nexus Future Systems is seeking a visionary Senior AI/LLM Engineer to join our elite R&D division. We are building the next generation of generative intelligence, and we need a technical mastermind to lead our model architecture and deployment strategies.
In this high-impact role, you will bridge the gap between theoretical AI research and scalable production systems. You will be responsible for fine-tuning large language models, optimizing inference latency, and architecting robust RAG (Retrieval-Augmented Generation) pipelines that handle millions of requests daily. If you are passionate about the future of artificial intelligence and want to work on products that will define the era of 2026, we want to hear from you.
Why Join Us?
- Work with state-of-the-art AI models and cutting-edge hardware.
- Competitive equity package and annual bonus structure.
- Flexible remote-first culture with a focus on innovation.
Responsibilities
- Design, train, and fine-tune large language models (LLMs) using PyTorch and TensorFlow.
- Architect and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise applications.
- Implement rigorous evaluation frameworks to ensure model accuracy, safety, and bias mitigation.
- Collaborate with cross-functional teams (Product, Data Science, Engineering) to define AI product requirements.
- Deploy models to production environments using MLOps tools (MLflow, Kubeflow) and cloud infrastructure (AWS/GCP).
- Stay ahead of the curve in AI research, evaluating new architectures like Mamba or Vision Transformers.
Qualifications
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in building and deploying machine learning models.
- Deep expertise in Python, C++, or CUDA for high-performance computing.
- Strong experience with Hugging Face Transformers, LangChain, and Vector Databases (Pinecone, Milvus).
- Experience with cloud deployment (AWS SageMaker, Google Vertex AI) and containerization (Docker, Kubernetes).
- Proven track record of optimizing inference speeds and reducing model latency.