Job Description
Join the Architects of Tomorrow. Nexus Innovations is pioneering the next generation of Artificial Intelligence with our flagship initiative, Project 2026. We are looking for a visionary Senior AI Engineer to build scalable, robust, and ethical machine learning systems that will define the technological landscape of the future.
In this role, you won't just maintain the status quo; you will break barriers. You will work directly with our CTO and lead a team of elite data scientists to deploy Large Language Models (LLMs) and generative AI solutions that solve complex real-world problems. If you are passionate about the future and ready to leave a legacy in the tech world, we want to meet you.
Responsibilities
- Architect and implement end-to-end machine learning pipelines for the Project 2026 initiative, focusing on scalability and low-latency inference.
- Collaborate with cross-functional teams including product managers, designers, and backend engineers to integrate AI models into production applications.
- Conduct rigorous experimentation, model tuning, and validation to ensure high accuracy and robustness across diverse datasets.
- Define and enforce best practices for MLOps, data governance, and ethical AI usage to mitigate bias and ensure compliance.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation within the engineering team.
- Stay ahead of the curve by researching emerging AI paradigms and evaluating their potential applicability to Nexus Innovations.
Qualifications
- Master’s or PhD degree in Computer Science, Machine Learning, or a related quantitative field (or equivalent professional experience).
- 5+ years of professional experience in AI/ML engineering, with a strong portfolio of deployed production models.
- Deep proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.
- Extensive experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Strong understanding of NLP, Transformers, and LLM fine-tuning techniques.
- Experience with MLOps tools (MLflow, Airflow, Kubeflow) and data pipeline management.