Job Description
Join the Architects of the Future
Nexus Future Technologies is pioneering the next era of intelligent systems. As a Senior AI/ML Engineer, you won't just be maintaining legacy models; you will be building the foundation for the AI landscape of 2026 and beyond. We are looking for visionaries who thrive on complexity and are driven to solve the world's most challenging problems with data.
Why Join Us?
We offer a competitive compensation package, fully remote flexibility, and the opportunity to work with top-tier talent on projects that redefine industry standards. If you are passionate about Deep Learning, NLP, and Generative AI, this is your chance to make a lasting impact.
Responsibilities
- Model Development: Design, train, and deploy scalable machine learning and deep learning models using Python, TensorFlow, and PyTorch.
- System Architecture: Architect robust MLOps pipelines to ensure efficient model training, validation, and productionization.
- Research & Innovation: Stay at the forefront of AI research, implementing state-of-the-art algorithms and techniques to improve model accuracy and performance.
- Collaboration: Partner with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Optimization: Continuously monitor and optimize model latency, throughput, and resource efficiency in cloud environments.
- Mentorship: Guide junior engineers and contribute to a culture of technical excellence and knowledge sharing.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, Statistics, or a related field (PhD preferred).
- Experience: 5+ years of professional experience in AI/ML engineering with a strong portfolio of deployed models.
- Technical Skills: Proficiency in Python, SQL, and experience with major deep learning frameworks (TensorFlow, PyTorch, JAX).
- Domain Expertise: Strong background in Natural Language Processing (NLP) or Computer Vision.
- Tools: Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Soft Skills: Exceptional problem-solving abilities and the ability to communicate complex technical concepts to non-technical stakeholders.