Job Description
Welcome to Nexus Future Labs, where we are pioneering the next generation of intelligent systems. We are looking for a visionary Generative AI Architect to lead our research and engineering teams in designing scalable, secure, and ethically sound AI solutions. In this role, you will be at the forefront of the 2026 technology landscape, bridging the gap between theoretical AI research and practical, high-impact applications.
Why Join Us?
At Nexus Future Labs, we value innovation, autonomy, and impact. You will work with state-of-the-art Large Language Models (LLMs), build custom AI agents, and define the architectural standards for our next-generation products. If you are passionate about shaping the future of human-machine interaction, we want to hear from you.
Responsibilities
- Architect Design: Design and implement robust, scalable generative AI architectures using Python, TensorFlow, and PyTorch.
- Model Optimization: Fine-tune and optimize pre-trained models for specific enterprise use cases, focusing on latency and cost-efficiency.
- RAG Implementation: Develop and deploy Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Ethical AI: Establish guidelines and frameworks to ensure AI outputs are unbiased, transparent, and aligned with safety protocols.
- System Integration: Integrate AI models into broader software ecosystems, working closely with DevOps and Data Engineering teams.
- Research & Prototyping: Stay ahead of industry trends by exploring emerging technologies like multimodal AI and agentic workflows.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related field.
- Experience: Minimum of 5 years of experience in software engineering, with at least 3 years specifically focused on Machine Learning and Generative AI.
- Technical Skills: Proficiency in deep learning frameworks (PyTorch, TensorFlow) and experience with LLMs (GPT, LLaMA, Claude).
- Cloud Expertise: Strong experience deploying models on AWS, GCP, or Azure using containerization (Docker, Kubernetes).
- Data Fluency: Deep understanding of vector databases, data preprocessing, and feature engineering.
- Communication: Excellent ability to translate complex technical concepts for non-technical stakeholders.