Job Description
Are you ready to define the technological landscape of 2026? Nexus Innovations is seeking a visionary Lead AI/ML Engineer to spearhead our next-generation generative AI initiatives. In this pivotal role, you will not merely implement existing solutions; you will architect the intelligent systems that will redefine human-computer interaction for the future.
We are a fast-paced, forward-thinking technology firm focused on building scalable, ethical, and high-performance AI models. As we look toward 2026, we need a leader who is passionate about pushing the boundaries of Large Language Models (LLMs), Autonomous Agents, and Deep Learning. If you thrive in an environment where innovation is the currency and code is your canvas, we want to hear from you.
Why Join Us?
• Work on cutting-edge AI infrastructure that will power the next decade of tech.
• Competitive equity package and top-tier compensation.
• Flexible remote-first culture with a collaborative office in the heart of San Francisco.
Responsibilities
- Design and deploy scalable machine learning architectures, with a specific focus on Large Language Models (LLMs) and multi-modal AI agents.
- Lead the end-to-end machine learning lifecycle (MLL), from data ingestion and feature engineering to model training, evaluation, and production deployment.
- Architect robust MLOps pipelines to ensure model reliability, real-time monitoring, and continuous learning in dynamic production environments.
- Collaborate closely with product managers and engineers to translate complex 2026 strategic roadmaps into technical reality.
- Drive innovation in ethical AI, ensuring our models are transparent, bias-free, and compliant with global data regulations.
- Conduct advanced research into novel neural architectures and optimization techniques to improve model efficiency.
- Mentor junior engineers and foster a culture of technical excellence within the data science team.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Expert proficiency in Python and deep frameworks such as PyTorch or TensorFlow.
- Proven track record of deploying production-grade AI applications and serving models via REST APIs.
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP), and containerization (Docker/Kubernetes).
- Familiarity with prompt engineering, fine-tuning large foundation models, and RAG architectures.
- Experience with high-availability systems and performance optimization.