Job Description
We are on a mission to architect the intelligent systems of tomorrow. As we look toward the horizon of 2026, FutureScale Technologies is seeking a visionary Senior Generative AI Engineer to lead our Research & Development division. You will not merely apply existing tools; you will define the next generation of Large Language Models (LLMs) and multimodal AI systems that will power enterprise solutions for a decade to come.
In this pivotal role, you will bridge the gap between theoretical AI research and scalable production engineering. If you are passionate about pushing the boundaries of what is possible in Generative AI and want to shape the future of human-machine interaction, we want to hear from you.
Responsibilities
- Design, train, and fine-tune large-scale Generative AI models using PyTorch and TensorFlow, focusing on performance and scalability.
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines to enhance knowledge accuracy and reduce hallucinations.
- Implement advanced prompt engineering strategies and model alignment techniques to ensure ethical and safe AI outputs.
- Collaborate with cross-functional teams to integrate AI capabilities into existing software ecosystems and product roadmaps.
- Conduct rigorous research to stay ahead of industry trends, particularly those emerging in the 2026 AI landscape.
- Monitor model performance in production environments, conducting A/B testing and iterative improvements.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- Proven experience (5+ years) in developing, deploying, and optimizing deep learning models at scale.
- Expert proficiency in Python, PyTorch, and machine learning frameworks.
- Deep understanding of Natural Language Processing (NLP) and transformer architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Strong grasp of MLOps practices, including model versioning, CI/CD, and infrastructure as code.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.