Job Description
We are seeking a visionary Senior AI/ML Engineer to join Nexus Future Labs and help architect the technological landscape for the year 2026 and beyond. As a pioneer in next-generation intelligence, we are building the foundational models and scalable infrastructure that will define the future of human-computer interaction.
In this high-impact role, you will not just implement existing solutions; you will define the 2026 Tech Roadmap, pushing the boundaries of Generative AI, Large Language Models (LLMs), and autonomous agent systems. You will work in a fast-paced, elite engineering environment focused on solving the most complex challenges in artificial intelligence.
Why Join Us?
We offer a competitive compensation package, equity packages, and the opportunity to work on cutting-edge technology that will shape the next decade of the industry.
Responsibilities
- Architect Future-Proof Systems: Design and implement scalable machine learning infrastructure capable of supporting production workloads for 2026 and beyond.
- Lead Model Development: Spearhead the research and development of proprietary Large Language Models and Generative AI agents.
- Optimize Performance: Drive optimization initiatives to reduce inference latency and improve model accuracy through advanced training techniques.
- Collaborate with Visionaries: Partner with product managers and data scientists to translate business requirements into robust technical solutions.
- Build MLOps Pipelines: Establish automated CI/CD pipelines for model training, validation, and deployment.
- Mentorship: Guide a team of junior engineers and data scientists, fostering a culture of technical excellence and innovation.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field.
- Experience: 5+ years of professional experience in AI/ML engineering with a proven track record of shipping production-level models.
- Programming: Expert-level proficiency in Python, C++, and experience with deep learning frameworks (PyTorch or TensorFlow).
- Architecture: Deep understanding of distributed systems, cloud architecture (AWS/GCP), and containerization (Docker/Kubernetes).
- Tech Stack: Experience with Vector Databases, RAG (Retrieval-Augmented Generation), and LLM fine-tuning.
- Problem Solving: Exceptional analytical skills with a focus on solving complex algorithmic problems.