Job Description
Are you ready to architect the future of intelligence? Join 2026 Systems, a pioneering force in next-generation artificial intelligence. We are looking for a visionary Senior Generative AI Engineer to lead the development of scalable, ethical, and high-impact AI models that will define the technology landscape of the coming decade.
In this pivotal role, you will bridge the gap between cutting-edge research and real-world application. You will work on proprietary LLMs, fine-tuning strategies, and the infrastructure that powers our ecosystem. If you are passionate about pushing the boundaries of what is possible with AI and want to shape the future of the industry, we want to hear from you.
Responsibilities
- Lead Model Development: Spearhead the research, design, and implementation of state-of-the-art Generative AI models, focusing on Large Language Models (LLMs) and multimodal architectures.
- Optimization & Scalability: Architect and optimize training pipelines to ensure models are efficient, cost-effective, and scalable across distributed computing environments.
- Ethical AI Implementation: Integrate fairness, accountability, and transparency principles into model design to mitigate bias and ensure responsible AI deployment.
- Technical Mentorship: Guide a team of junior data scientists and engineers, fostering a culture of innovation, continuous learning, and technical excellence.
- Collaboration: Partner closely with product managers, designers, and engineers to translate complex research findings into deployable product features.
- Research: Stay at the forefront of the industry by publishing papers and contributing to open-source communities relevant to AI advancement.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related quantitative field.
- Experience: Minimum of 5+ years of professional experience in machine learning, deep learning, or AI engineering.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX; strong experience with Hugging Face Transformers, LangChain, or similar frameworks.
- Model Fine-tuning: Demonstrated expertise in fine-tuning large language models (e.g., LLaMA, GPT variants) and RLHF (Reinforcement Learning from Human Feedback).
- Cloud Infrastructure: Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Exceptional ability to debug complex system architectures and drive solutions in ambiguous environments.