Job Description
Are you a visionary engineer passionate about the next generation of artificial intelligence? Nexus Future Labs is seeking a Senior Generative AI Engineer to lead our breakthrough initiatives in Large Language Models (LLMs) and autonomous agents.
In this pivotal role, you will architect and deploy state-of-the-art AI systems that redefine user interaction and automate complex workflows. You will work at the intersection of research and engineering, collaborating with world-class data scientists and product leaders to build the future of intelligent software.
Why Join Us?
At Nexus Future Labs, we are not just building software; we are engineering the fabric of tomorrow. You will have the autonomy to experiment with the latest architectures, influence our engineering culture, and drive the adoption of Generative AI across the enterprise.
Responsibilities
- Model Development: Design, train, and fine-tune cutting-edge Generative AI models using transformer architectures and state-of-the-art frameworks.
- RAG Implementation: Build and optimize Retrieval-Augmented Generation pipelines to enhance factual accuracy, reduce hallucinations, and maintain context relevance.
- System Optimization: Engineer high-performance inference systems capable of handling real-time, high-volume requests with low latency.
- Research & Innovation: Stay ahead of the curve by exploring emerging techniques in multimodal learning, reinforcement learning, and agent-based systems.
- Collaboration: Partner with cross-functional teams to translate complex AI capabilities into scalable, user-centric software products.
- Productionization: Manage the full ML lifecycle from data engineering to deployment, monitoring, and retraining strategies.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering, with at least 3 years focused on Machine Learning or Deep Learning.
- Technical Skills: Expert proficiency in Python, PyTorch, or TensorFlow; solid understanding of NLP fundamentals and deep learning architectures.
- Deployment: Experience with MLOps tools (MLflow, Kubeflow, DVC) and cloud platforms (AWS, GCP, or Azure) for scalable model deployment.
- Problem Solving: Demonstrated ability to tackle ambiguous problems and deliver robust, production-ready solutions under tight deadlines.