Job Description
We are seeking a visionary Senior AI Engineer to join our elite team at Nexus Future Labs. As we pioneer the next generation of generative intelligence, we need a technical leader who is obsessed with scalability, efficiency, and pushing the boundaries of what's possible in 2026.
In this role, you will architect robust machine learning pipelines, optimize large language models (LLMs) for real-time applications, and mentor a team of brilliant engineers. You will work directly with our product leads to define the roadmap for our flagship AI platform. If you are passionate about the future of technology and want to build the tools that will define the next decade, this is your opportunity.
Why Join Us?
- Work on cutting-edge AI infrastructure in the heart of Silicon Valley.
- Competitive compensation and equity packages.
- Flexible remote-first hybrid culture.
Responsibilities
- Architect and deploy scalable machine learning models and neural networks to production environments using Python and TensorFlow/PyTorch.
- Conduct research on novel AI architectures, specifically focusing on LLM optimization and NLP advancements.
- Collaborate with cross-functional teams (Data Science, Product, DevOps) to integrate AI features seamlessly into user-facing products.
- Optimize existing models for latency, throughput, and memory efficiency to ensure high performance under heavy load.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Implement MLOps best practices including CI/CD, model versioning, and automated retraining pipelines.
Qualifications
- B.S., M.S., or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
- 7+ years of professional experience in machine learning engineering or applied AI research.
- Deep proficiency in Python, SQL, and modern deep learning frameworks (TensorFlow, PyTorch, Keras).
- Extensive experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Strong understanding of distributed systems, data structures, and algorithmic complexity.
- Experience with MLOps tools (MLflow, Kubeflow) and big data technologies (Spark, Kafka).