Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Future Labs is on a mission to redefine the boundaries of Artificial Intelligence. As we prepare for the next generation of AGI (Artificial General Intelligence) by 2026, we are seeking a visionary Senior AI Engineer to lead our advanced model development initiatives.
In this role, you will not just write code; you will shape the future of human-machine interaction. You will work on cutting-edge Generative AI models, optimize large-scale inference systems, and build the robust infrastructure required to support the next wave of technological disruption.
If you are passionate about Deep Learning, NLP, and building systems that learn, we want to meet you.
Responsibilities
- Model Architecture & Development: Design, train, and fine-tune large-scale transformer models (LLMs) to solve complex, real-world problems.
- Performance Optimization: Implement advanced optimization techniques (quantization, distillation, pruning) to deploy models efficiently on cloud infrastructure.
- MLOps Implementation: Build and maintain CI/CD pipelines for machine learning models, ensuring reproducibility and scalability.
- Research & Innovation: Stay ahead of the curve by researching the latest papers in AI and evaluating new architectures for integration into our product suite.
- Cross-Functional Collaboration: Partner with product managers and data scientists to translate technical requirements into scalable engineering solutions.
- RAG Systems: Develop and deploy Retrieval-Augmented Generation systems to enhance model accuracy and reduce hallucinations.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Technical Proficiency: Deep expertise in Python, PyTorch, or TensorFlow.
- Experience: Minimum of 5+ years of experience in AI/ML engineering, with a proven track record of deploying production-ready models.
- Frameworks: Strong hands-on experience with Hugging Face, LangChain, or similar orchestration tools.
- Infrastructure: Proficiency in cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Exceptional analytical skills with the ability to debug complex distributed systems and optimize data pipelines.