Career Details

Job Description:

Responsibilities:

AI & Machine Learning Research

• Conduct state-of-the-art research in AI/ML, including deep learning, reinforcement learning, generative AI, and NLP.
• Explore and develop novel AI models, algorithms, and architectures to solve complex real-world problems.
• Publish research findings in top-tier AI/ML conferences and journals.

Model Development & Experimentation

• Design, train, and optimize AI/ML models using frameworks like TensorFlow, PyTorch, JAX, and Hugging Face Transformers.
• Prototype scalable and efficient AI algorithms for applications in computer vision, NLP, speech recognition, recommendation systems, or financial AI.
• Implement hyperparameter tuning, model evaluation, and performance optimization for production-ready AI models.

AI Product Innovation & Strategy

• Collaborate with data scientists, engineers, and product teams to translate research insights into AI-driven applications and solutions.
• Develop AI-powered prototypes and proof-of-concepts (PoCs) for potential integration into enterprise AI products.
• Stay ahead of AI trends and emerging technologies, identifying new opportunities for innovation and strategic growth.

MLOps & AI Deployment

• Work with MLOps teams to deploy research models into scalable cloud-based AI/ML pipelines.
• Ensure model robustness, fairness, and interpretability, implementing best practices for AI governance and responsible AI.
• Optimize AI models for real-time inference, distributed computing, and edge AI deployments.

Technical Leadership & Mentorship

• Provide technical mentorship to junior AI researchers and data scientists.
• Lead AI workshops, knowledge-sharing sessions, and contribute to open-source AI research initiatives.
• Engage with academic institutions, AI communities, and industry partners to foster research collaborations.

Preferred Qualifications:

✔ Ph.D. or Master's in AI, Computer Science, Mathematics, or a related field.
✔ 5+ years of hands-on AI/ML research experience in an academic, industry, or applied research setting.
✔ Strong expertise in deep learning architectures (Transformers, GANs, CNNs, RNNs, LSTMs, etc.).
✔ Experience with large-scale AI/ML training, distributed computing, and GPU-accelerated ML frameworks.
✔ Proficiency in Python, TensorFlow, PyTorch, JAX, and ML libraries like Scikit-learn, OpenAI Gym, etc..
✔ Knowledge of AI ethics, bias mitigation, explainable AI (XAI), and model interpretability techniques.
✔ Prior experience in patent filings, AI publications, or presenting at AI/ML conferences is a plus.

Join us to pioneer groundbreaking AI research and shape the future of intelligent systems!