Job Description
Are you ready to architect the future of intelligence? Nexus Future Labs is seeking a visionary Senior AI Architect to lead our charge into 2026 and beyond. We are building the next generation of generative AI systems that will redefine human-computer interaction. If you thrive in a high-impact, fast-paced environment and possess a deep understanding of Large Language Models (LLMs), this is your opportunity to shape the technological landscape.
In this role, you will not just implement existing models; you will push the boundaries of what is possible. You will be responsible for designing scalable, secure, and efficient AI infrastructures that power our flagship products. Join us in defining the roadmap for artificial general intelligence applications.
Responsibilities
- Architect and deploy scalable LLM and Generative AI solutions using state-of-the-art frameworks (e.g., PyTorch, TensorFlow, JAX).
- Design and optimize model inference pipelines to ensure low latency and high throughput for real-time applications.
- Lead technical strategy for AI infrastructure, ensuring alignment with long-term business goals and 2026 vision.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate research into production-ready products.
- Implement rigorous testing and evaluation frameworks to ensure model accuracy, safety, and fairness.
- Mentor junior engineers and establish best practices for MLOps and model governance.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related technical field.
- 7+ years of experience in software engineering, with at least 3 years focused on AI/ML architecture and model deployment.
- Deep expertise in Natural Language Processing (NLP), Transformer architectures, and fine-tuning large language models.
- Proficiency in Python, SQL, and experience with cloud platforms (AWS, GCP, or Azure).
- Strong understanding of distributed systems, high availability, and system scalability.
- Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow) and containerization (Docker, Kubernetes).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.