Job Description
We are seeking a visionary Senior AI/ML Engineer to join our elite engineering team in San Francisco. At Nexus Future Tech, we are building the next generation of generative intelligence systems designed to solve complex problems for enterprise clients. If you are passionate about pushing the boundaries of what's possible with Large Language Models (LLMs), computer vision, and predictive analytics, we want to hear from you.
In this role, you will lead the architecture and implementation of scalable machine learning infrastructure. You will work closely with cross-functional teams to deploy state-of-the-art models that drive our product roadmap. This is an opportunity to shape the technological landscape of 2026 and beyond.
Responsibilities
- Design & Development: Architect and implement robust, scalable machine learning pipelines using Python, PyTorch, and TensorFlow.
- Model Optimization: Fine-tune and optimize large language models (LLMs) for production environments, focusing on latency, throughput, and cost efficiency.
- Research & Innovation: Stay at the forefront of AI research, evaluating new architectures and techniques to integrate into our core platform.
- Deployment: Manage the end-to-end CI/CD lifecycle of AI models, ensuring reliability and monitoring in cloud environments (AWS/GCP).
- Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Collaboration: Partner with product managers and stakeholders to define technical requirements and translate business needs into AI solutions.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field.
- Experience: 5+ years of professional experience in machine learning, deep learning, or artificial intelligence.
- Technical Skills: Proficiency in Python, SQL, and experience with deep learning frameworks (PyTorch, TensorFlow, JAX).
- LLM Experience: Proven track record of working with transformer architectures, RAG (Retrieval-Augmented Generation), and fine-tuning models like GPT, Llama, or Claude.
- Infrastructure: Strong understanding of cloud computing, containerization (Docker, Kubernetes), and MLOps practices.
- Problem Solving: Ability to debug complex distributed systems and optimize algorithmic performance.