Job Description
Are you ready to architect the intelligence of tomorrow? Nexus Future Systems is seeking a visionary Senior Generative AI Engineer to lead our next-generation AI initiatives. In this pivotal role, you will not just build models; you will define the future of human-machine interaction, ensuring our solutions remain at the cutting edge of the industry by 2026 and beyond.
We are a team of elite engineers, researchers, and product strategists dedicated to pushing the boundaries of what is possible with Large Language Models (LLMs) and generative architectures. If you thrive in a fast-paced, high-impact environment and possess an obsessive attention to detail, we want to hear from you.
Why Join Us?
- Impactful Work: Build AI solutions that will redefine how businesses operate globally.
- Competitive Compensation: Base salary $180k - $250k plus equity and performance bonuses.
- State-of-the-Art Tools: Access to the latest GPUs and cloud infrastructure.
- Culture of Innovation: A flat hierarchy where your ideas drive product direction.
Responsibilities
- Architect, train, and fine-tune state-of-the-art Large Language Models (LLMs) and generative AI models tailored for enterprise applications.
- Optimize model inference pipelines to ensure low-latency, high-throughput performance in production environments.
- Collaborate closely with cross-functional teams (Product, Design, Data Science) to translate complex business requirements into robust AI solutions.
- Implement rigorous evaluation frameworks and metrics to continuously monitor model accuracy, bias, and safety.
- Stay abreast of the latest research in Deep Learning and Natural Language Processing (NLP) to integrate cutting-edge advancements.
- Conduct code reviews and mentor junior engineers, fostering a culture of technical excellence and continuous learning.
Qualifications
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
- Minimum of 5 years of professional experience in Machine Learning, Deep Learning, or AI Engineering.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying and scaling AI models in production environments.
- Deep understanding of Transformer architectures, NLP, and prompt engineering strategies.
- Experience with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).