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Information Technology 🏒 Full Time ⭐️ Verified

Senior Generative AI Engineer

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
18 Mei 2026
Deadline
18 Mei 2027

Job Description

We are at the forefront of the AI revolution, building the intelligent systems that will define the year 2026 and beyond. Nexus Future Labs is seeking a visionary Senior Generative AI Engineer to join our elite engineering team. You will be responsible for designing, training, and deploying state-of-the-art Large Language Models (LLMs) and multimodal AI systems that solve complex real-world problems.

In this role, you will work closely with product leaders and researchers to translate cutting-edge academic research into scalable, production-ready software. If you are passionate about the future of AI, have a deep understanding of machine learning architectures, and want to leave a lasting impact on how humans interact with machines, we want to hear from you.

Responsibilities

  • Design, implement, and optimize Generative AI models (LLMs, GANs, Diffusion models) for high-scale production environments.
  • Develop and fine-tune foundation models using PyTorch and TensorFlow to improve accuracy, latency, and cost-efficiency.
  • Build Retrieval-Augmented Generation (RAG) pipelines and vector databases to enhance model context and reduce hallucinations.
  • Collaborate with cross-functional teams to integrate AI capabilities into consumer-facing products and enterprise solutions.
  • Establish best practices for model monitoring, evaluation, and ethical AI deployment.
  • Research and prototype novel architectures to stay ahead of industry trends.

Qualifications

  • Master’s or PhD in Computer Science, Machine Learning, or a related field (or equivalent practical experience).
  • 5+ years of professional experience in software engineering with a strong focus on AI/ML.
  • Deep proficiency in Python and major ML frameworks (PyTorch, TensorFlow, JAX).
  • Extensive experience with NLP libraries (Hugging Face, NLTK, SpaCy) and LLM APIs (OpenAI, Anthropic, Cohere).
  • Strong understanding of distributed systems, cloud architecture (AWS/Azure/GCP), and containerization (Docker/Kubernetes).
  • Proven track record of deploying models that handle high concurrency and low latency.

Required Skills

Python PyTorch TensorFlow NLP Large Language Models LLM Fine-tuning MLOps Docker Kubernetes AWS Machine Learning Deep Learning

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