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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI Engineer - Generative Models

Nexus AI Solutions
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
12 Mei 2026
Deadline
12 Mei 2027

Job Description

We are on a mission to revolutionize the enterprise landscape through next-generation artificial intelligence. Nexus AI Solutions is seeking a visionary Senior AI Engineer to lead our cutting-edge research and development team. If you are passionate about building scalable, robust, and ethically sound AI systems that push the boundaries of what is possible, we want to meet you.

In this role, you will bridge the gap between theoretical research and production deployment, working on large language models (LLMs), agentic workflows, and multimodal systems. Join a team of world-class engineers and researchers dedicated to shaping the future of technology in 2026 and beyond.

Responsibilities

  • Design, develop, and deploy state-of-the-art machine learning models, with a focus on Generative AI and Large Language Models.
  • Collaborate with cross-functional teams of data scientists, engineers, and product managers to define AI product requirements and roadmap.
  • Optimize model inference latency and resource efficiency to ensure seamless integration into production environments.
  • Conduct rigorous research to advance the state-of-the-art in natural language understanding and generation.
  • Establish and enforce best practices for data privacy, model security, and ethical AI usage.
  • Mentor junior engineers and contribute to the technical architecture of the AI platform.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
  • 5+ years of professional experience in machine learning, deep learning, or artificial intelligence.
  • Strong proficiency in Python and deep familiarity with PyTorch or TensorFlow.
  • Proven track record of deploying machine learning models to production at scale.
  • Experience with vector databases, RAG architectures, and fine-tuning LLMs (e.g., GPT-4, Llama 3).
  • Deep understanding of MLOps pipelines, cloud infrastructure (AWS/Azure/GCP), and containerization (Docker/Kubernetes).
  • Excellent communication skills with the ability to translate complex technical concepts for diverse audiences.

Required Skills

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

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