Job Description
Are you ready to architect the technological landscape of the future? Nexus Future Labs is seeking a visionary Senior AI Engineer to lead our cutting-edge initiatives in Generative AI and Autonomous Systems. As we look ahead to the 2026 tech horizon, we are building the next generation of intelligent agents and multimodal models that will redefine human-computer interaction.
In this role, you will not just deploy models; you will define the architecture for the AI-native applications of tomorrow. You will work at the intersection of deep learning, MLOps, and ethical AI, ensuring our solutions are scalable, robust, and aligned with the rapid pace of innovation expected in 2026 and beyond.
Why join us?
- Work on next-gen AI infrastructure.
- Competitive equity and salary package.
- Flexible remote-first culture.
Responsibilities
- Architect & Deploy: Design and implement scalable AI architectures for large-scale production environments using PyTorch and TensorFlow.
- Model Optimization: Fine-tune and optimize Large Language Models (LLMs) and diffusion models to achieve high performance and low latency.
- Research & Innovation: Lead research initiatives in emerging areas such as Agentic Workflows, Reinforcement Learning from Human Feedback (RLHF), and Multimodal Learning.
- MLOps Strategy: Build and maintain CI/CD pipelines for machine learning, ensuring automated training, evaluation, and deployment cycles.
- Ethical AI: Implement robust guardrails and safety protocols to ensure AI outputs are fair, unbiased, and compliant with evolving regulations.
- Mentorship: Guide a team of junior data scientists and ML engineers, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or AI Engineering.
- Programming: Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, JAX).
- AI Specialization: Deep expertise in Natural Language Processing (NLP), Computer Vision, or Generative AI models.
- Tools: Strong experience with cloud platforms (AWS, GCP, or Azure), Kubernetes, and data orchestration tools (Airflow, Kubeflow).
- Communication: Excellent ability to translate complex technical concepts into clear, actionable insights for cross-functional stakeholders.