AI Engineer
About Skan AI
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 looking for an AI Engineer with exceptional problem-solving skills and a strong foundation in classical algorithms, data science, machine learning and deep learning to join our core Data Science team. You will design, build, and optimize AI/ML systems that analyze massive volumes of sequential event data captured from enterprise desktop activity, turning raw interaction streams into structured process understanding, variant analysis, and actionable operational insights.
Key Responsibilities
Tackle complex, open-ended problems where business processes must be reverse-engineered from noisy, incomplete, and high-volume event streams; there is no textbook answer
- - you define the approach.
- Design efficient algorithms for process discovery at scale, handling billions of events, millions of unique traces, and thousands of activity variants while maintaining sub-second query performance.
- Design and implement models for mining, clustering, and classifying sequential activity data (event logs, user interaction sequences, clickstreams) at enterprise scale.
- Build ML models to classify and label user activities from raw desktop observation data (screenshots, UI metadata, application events) using computer vision and NLP.
- Train, evaluate, and deploy models including sequence-to-sequence architectures (Transformers, LSTMs, HMMs), graph neural networks and clustering algorithms for process pattern recognition.
- Apply techniques from temporal data mining, e.g., sequential pattern mining, episode mining, and time-series analysis to extract insights from event streams.
- Leverage LLMs and generative AI for intelligent summarization, anomaly explanation, and natural-language querying of process data.
- Debug and reason through edge cases in complex data pipelines where subtle algorithmic errors can cascade into incorrect process maps affecting enterprise decisions.
- Partner with product, UX, and customer-facing teams to translate business process challenges into well-defined algorithmic and ML problem statements.
- Contribute to Skan's agentic AI capabilities, building models that not only discover inefficiencies but recommend and orchestrate automated actions.
- Stay current with advances in AI. Research and experiment with state-of-the-art AI techniques to improve existing models and integrate cutting-edge techniques into production systems.
Qualifications & Skills
Required:
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Bachelor's/Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.
- Exceptional problem-solving ability: You excel at breaking down ambiguous, complex problems into tractable sub-problems and designing clean algorithmic solutions.
- Strong proficiency in Python and experience with PyTorch or TensorFlow, scikit-learn, pandas, and SQL.
- Deep 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.).
- Familiarity with graph algorithms and graph-based modeling (traversal, shortest path, community detection, graph neural networks).
- Experience with LLMs and knowledge of multi-modal AI (text, image).
Hands-on experience building and deploying ML/AI models or algorithm-heavy systems in production.
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Nice to Have:
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Strong performance in competitive programming, algorithmic challenges, or systems design interviews is a signal we value.
- Familiarity with process mining concepts, event logs, process discovery algorithms, conformance checking, and variant analysis. Experience with PM4Py, ProM, or similar tools is a plus.
- Knowledge of cloud computing (AWS, GCP, Azure) and MLOps tools.
- Familiarity with agentic AI frameworks and LLM-based tool-use patterns (LangChain, function calling, RAG pipelines).
