Job Description
Are you ready to shape the future of technology?
Nexus Future Labs is seeking a visionary Senior AI Architect to lead our cutting-edge research and development initiatives. We are building the infrastructure for the next generation of Generative AI, Large Language Models, and Autonomous Systems. If you are passionate about pushing the boundaries of what is possible in 2026 and beyond, we want to meet you.
In this role, you will design scalable, high-performance AI systems that redefine industry standards. You will collaborate with world-class engineers, researchers, and product managers to deliver solutions that impact millions of users worldwide.
Why join us?
- Work on projects that define the roadmap for AI evolution.
- Competitive compensation package and equity options.
- Flexible remote-first culture with state-of-the-art equipment.
- Opportunity to mentor the next generation of AI talent.
Responsibilities
- Design and architect scalable machine learning infrastructure capable of supporting high-volume inference and training workloads.
- Lead research and implementation of state-of-the-art Generative AI models, including Transformers and diffusion models.
- Optimize existing models for latency, throughput, and cost-efficiency in production environments.
- Collaborate with data science teams to develop novel algorithms and proprietary datasets.
- Establish best practices for MLOps, including model versioning, monitoring, and deployment pipelines.
- Drive technical decision-making and evaluate emerging technologies to ensure our stack remains future-proof.
- Provide mentorship and technical guidance to junior engineers and data scientists.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
- Minimum of 5+ years of professional experience in AI/ML engineering, with a focus on Deep Learning.
- Extensive proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of Natural Language Processing (NLP) and LLM architectures (e.g., GPT, BERT).
- Experience with distributed computing frameworks such as Kubernetes, Ray, or Spark.
- Strong grasp of software engineering principles, including version control (Git), CI/CD, and testing.
- Demonstrated ability to translate complex technical concepts into actionable roadmaps for cross-functional teams.