AI/ML Architect

Greetings From Photon,

We hope you are good! We are hiring AI/ML Architect to join our Digital Engineering team.

Who are we?

For the past 20 years, we have powered many Digital Experiences for the Fortune 500. Since 1999, we have grown from a few people to more than 4000 team members across the globe that are engaged in various Digital Modernization.

AI/ML Architect | Chicago, IL | Fulltime

Job Summary :

We are seeking a highly experienced AI / ML Engineer to design, develop, and deploy machinelearning solutions that power key analytics and intelligent automation across our fintech ecosystem. This role focuses on delivering productiongrade ML systems spanning classical ML, deep learning, and LLMbased applications while ensuring scalability, reliability, and regulatory compliance. The engineer will own endtoend model development, from data preparation through deployment and monitoring, and will work closely with engineering, product, and data teams to implement impactful AI capabilities.

What will you do?

• Build, train, and evaluate ML and deep learning models for classification, prediction, anomaly detection, and NLP use cases.
• Implement scalable ML pipelines for data processing, feature engineering, and inference.
• Develop and integrate LLMbased capabilities including embeddings, RAG workflows, and finetuned models.
• Deploy models to production using containerized and cloudnative infrastructures (Docker, Kubernetes, Azure/AWS).
• Implement MLOps practices including CI/CD integration, experiment tracking, model registries, and monitoring.
• Ensure highquality data pipelines and automate preprocessing for structured and unstructured data.
• Apply model explainability (SHAP, LIME) and Responsible AI principles to ensure transparency and safe use.

What are we looking for?

• Deep handson experience building and deploying ML models (supervised, unsupervised, deep learning, NLP).
• Strong Python skills (NumPy, Pandas, Scikitlearn, PyTorch or TensorFlow).
• Experience with SQL and dataengineering workflows (Spark or Airflow).
• Practical experience with LLMs, vector databases, embeddings, and RAG systems.
• Familiarity with production ML deployment using Docker, Kubernetes, CI/CD pipelines, and GPU acceleration.
• Experience with cloud AI tooling (Azure, AWS, or Google Cloud Platform) and scalable inference pipelines.
• Strong understanding of model monitoring, drift detection, and retraining strategies.
• Ability to create clear model explanations and apply interpretability tools.