Be at the Forefront of the Agentic AI Revolution
At Skan AI, we are pioneering the context engine for human and agentic execution, bringing context from enterprise operators, systems, and processes to power how the world's largest organizations execute their most complex, mission-critical work.
Why Skan AI
We're in hyper-growth mode at exactly the right moment in history. As enterprises race to adopt agentic AI, we're uniquely positioned to deliver the clear signal they desperately need: a platform that trains and grounds AI Agents in trillions of real execution signals, enabling reliable, compliant automation of their most complex processes.
Backed by Dell Technologies Capital and other leading investors, we're the only company that can bridge the gap between AI's promise and enterprise reality, making us perfectly positioned to define the agentic era for modern enterprises.
Our diverse, collaborative team of 250+ innovators is solving category-defining challenges at the intersection of AI, process intelligence, and enterprise work. Diverse perspectives fuel breakthrough thinking, cross-functional collaboration is the norm, and our work directly transforms how Fortune 500 companies operate. We are shaping the future of work itself.
Who are we looking for
We are seeking a highly skilled AI Engineer to design and implement intelligent systems that drive automation, insights, and innovation. You will work on machine learning (ML), deep learning and optimization algorithms collaborating with cross-functional teams building scalable AI solutions for real-world applications.
Key Responsibilities
- Design, develop and optimize ML/AI models for classification, regression, and recommendation systems.
- Implement machine learning pipelines (end-to-end), from data preprocessing to model deployment.
- Develop and fine-tune deep learning architectures using TensorFlow, PyTorch etc.
- Apply MLOps best practices for model versioning, monitoring, and retraining.
- Work with large-scale datasets, performing feature engineering and data augmentation.
- Optimize model performance for scalability, latency, and efficiency in production.
- Deploy AI models using MLOps practices, containerization (Docker), and cloud services (Azure).
- Research and experiment with state-of-the-art AI techniques to improve existing models and integrate cutting-edge techniques into production systems.
Collaborate with data scientists and product teams to integrate AI into business applications.
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Qualifications & Skills
Required:
- Bachelor's/Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
- Strong programming skills in Python
- Experience in training and deploying ML models in production.
- Proficiency with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face, Scikit-learn).
- Knowledge of cloud computing (AWS, GCP, Azure) and MLOps tools.
- Understanding of data structures, algorithms, and optimization techniques.
- Experience with data engineering, feature extraction, and model evaluation.
- Knowledge of deep learning techniques (CNNs, RNNs, Transformers, GANs, etc.).
- Experience with LLMs (GPT, BERT, LLaMA, T5) and fine-tuning models.
Knowledge of multi-modal AI (text, image).
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Nice to Have:
- Knowledge of reinforcement learning, planning, and decision-making models.
- Hands-on experience with big data processing and big data frameworks
- Familiarity with vector databases.
- Experience in real-time AI applications for high-performance systems.
