Job Description
We are seeking a visionary Senior Artificial Intelligence Engineer to join Nexus Future Systems, a pioneer in next-generation cognitive computing. As a key player in our R&D division, you will architect and deploy cutting-edge machine learning models that power our core product suite. We are looking for individuals who are passionate about the future of AI and are eager to solve complex problems at scale.
In this role, you will bridge the gap between theoretical research and production-grade engineering, ensuring our AI solutions are robust, scalable, and ethically responsible. If you are ready to shape the technology landscape of 2026 and beyond, we want to hear from you.
Responsibilities
- Model Development: Design, train, and optimize deep learning models (e.g., Transformers, CNNs, RNNs) for natural language processing and computer vision applications.
- Production Deployment: Deploy machine learning models to cloud infrastructure (AWS/GCP) using MLOps tools like Docker, Kubernetes, and MLflow for seamless scalability.
- Research & Innovation: Stay ahead of the curve by researching the latest advancements in AI, including Large Language Models (LLMs) and generative AI techniques.
- Code Optimization: Write clean, efficient, and production-ready code in Python, ensuring high performance and low latency in inference.
- Collaboration: Partner with data scientists, product managers, and software engineers to translate business requirements into technical AI solutions.
- Ethical AI: Implement guidelines to ensure AI fairness, transparency, and bias mitigation in our algorithmic outputs.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: Minimum of 5 years of professional experience in AI/ML engineering and software development.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with SQL and NoSQL databases.
- Deployment: Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies.
- Problem Solving: Demonstrated ability to troubleshoot complex system issues and optimize model performance in real-world scenarios.
- Communication: Excellent written and verbal communication skills with the ability to explain technical concepts to non-technical stakeholders.