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Senior AI Engineer (2026 Vision)

Zenith Tech
San Francisco
Estimated Salary
USD 160.000 – USD 220.000
New
Live Update
1 Juni 2026
Deadline
1 Jun 2027

Job Description

We are seeking a visionary Senior AI Engineer to join Zenith Tech. As we prepare for the next generation of intelligence, we are looking for a technical leader who can architect scalable, high-performance Machine Learning systems. This role is pivotal in defining our AI roadmap for 2026 and beyond, focusing on Generative AI and Large Language Models (LLMs).

In this position, you will not only build models but also ensure they are deployed securely, ethically, and efficiently at scale. You will work in a collaborative environment where innovation is encouraged, and your work will directly shape the future of our product suite.

Key Highlights:

  • Work with the latest hardware and cloud infrastructure (NVIDIA H100, AWS, GCP).
  • Competitive compensation package with performance bonuses.
  • Flexible remote-first policy with a hub in San Francisco.

Responsibilities

  • Lead the design and implementation of advanced NLP models, including fine-tuning and RAG architectures for LLMs.
  • Optimize model inference pipelines to ensure sub-millisecond latency and high throughput in production environments.
  • Collaborate with product managers and engineers to translate business requirements into technical AI solutions.
  • Establish and enforce best practices for MLOps, data governance, and model monitoring.
  • Conduct rigorous testing and validation of AI models to ensure accuracy, fairness, and robustness.
  • Mentor junior engineers and data scientists, fostering a culture of technical excellence.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field; PhD preferred.
  • Minimum of 5 years of professional experience in software engineering and machine learning.
  • Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
  • Deep understanding of Deep Learning principles, Natural Language Processing, and Transformer architectures.
  • Experience deploying models on cloud platforms (AWS, Azure, or GCP) using containerization tools like Docker and Kubernetes.
  • Proven track record of shipping production-grade ML applications.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning NLP LLMs MLOps Docker Kubernetes AWS GCP

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