Job Description
Are you ready to architect the future of intelligence? Nexus 2026 Innovations is seeking a visionary Lead Generative AI Engineer to pioneer the next generation of Large Language Models (LLMs) and autonomous agents. As we prepare for the transformative era of 2026, we need a technical leader to build scalable, ethical, and high-performance AI systems that redefine human-machine interaction.
In this role, you will not just implement existing solutions; you will define the architecture for the next wave of generative AI, collaborating with world-class researchers and engineers to push the boundaries of what is possible.
Why Join Nexus 2026?
- Impactful Work: Build AI systems that will power millions of interactions globally.
- Future-Proof Career: Stay at the cutting edge of technology leading up to and beyond 2026.
- Competitive Compensation: Industry-leading salary and equity package.
Join us in San Francisco and help shape the digital landscape of tomorrow.
Responsibilities
- Architect & Deploy: Design and implement scalable MLOps pipelines for training, fine-tuning, and deploying large-scale generative models on cloud infrastructure (AWS/GCP).
- Model Optimization: Lead research into model quantization, distillation, and optimization techniques to reduce latency and cost while maintaining high output quality.
- Ethical AI: Establish and enforce best practices for AI safety, bias mitigation, and responsible AI governance within the organization.
- Technical Leadership: Mentor a team of junior and senior engineers, conducting code reviews, architecture reviews, and technical strategy sessions.
- Integration: Work closely with product and engineering teams to integrate advanced AI capabilities into consumer-facing products.
- Research: Stay abreast of the latest advancements in NLP, transformers, and multimodal learning to drive innovation.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering with a focus on Machine Learning and Deep Learning.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, or JAX. Strong understanding of transformer architectures (BERT, GPT, T5).
- MLOps: Proven experience with MLflow, Kubeflow, or similar MLOps tools; familiarity with containerization (Docker/Kubernetes) and CI/CD pipelines.
- Problem Solving: Exceptional ability to troubleshoot complex system bottlenecks and optimize model inference performance.
- Communication: Excellent written and verbal communication skills, capable of translating complex technical concepts to stakeholders.