Job Description
Are you ready to define the next era of artificial intelligence?
We are Nexus Future Labs, a cutting-edge technology firm pioneering the integration of Generative AI into enterprise solutions. We are seeking a visionary Senior Generative AI Engineer to lead our research and development efforts. In this role, you will not just write code; you will architect the neural architectures that power the next generation of intelligent applications.
Why Join Us?
- Work on mission-critical projects that have a global impact.
- Competitive compensation package with equity options.
- Flexible remote and hybrid work arrangements.
- Access to state-of-the-art compute infrastructure and research grants.
If you are passionate about Large Language Models (LLMs), Reinforcement Learning from Human Feedback (RLHF), and scalable AI infrastructure, we want to hear from you.
Responsibilities
- Lead Model Development: Design, train, and fine-tune state-of-the-art Large Language Models (LLMs) and multimodal systems using PyTorch and TensorFlow.
- Optimize Performance: Implement distributed training strategies and optimize inference latency to handle high-volume production workloads.
- Research & Innovation: Stay at the forefront of AI research, exploring novel architectures and techniques to push the boundaries of model capabilities.
- Collaborate with Cross-Functional Teams: Partner with product managers, data scientists, and engineers to translate research findings into scalable, user-centric features.
- MLOps Implementation: Build and maintain robust MLOps pipelines ensuring reproducibility, monitoring, and automated deployment of AI models.
- Code Quality: Mentor junior engineers, conduct code reviews, and establish best practices for AI engineering within the organization.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering or machine learning, with at least 2 years focusing on NLP or Generative AI.
- Technical Skills: Deep proficiency in Python, PyTorch, and Hugging Face Transformers. Experience with distributed computing (Ray, Spark) and cloud platforms (AWS, GCP, Azure) is required.
- Model Training: Proven track record of training large-scale models from scratch or fine-tuning pre-trained models for specific domains.
- Problem Solving: Strong analytical skills with the ability to debug complex system-level issues and optimize algorithmic efficiency.
- Communication: Excellent verbal and written communication skills, capable of explaining complex technical concepts to non-technical stakeholders.