AI ML Architect

Location:
Dallas, TX (Must be open to travel to Photon client locations)

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. Our current focus and innovation in Digital Hyper expansion TM offers nearly limitless opportunities for career growth. For a brief 1-minute video about us, you can check out

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

• 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 LLM‑based capabilities including embeddings, RAG workflows, and fine‑tuned models.
• Deploy models to production using containerized and cloud‑native infrastructures (Docker, Kubernetes, Azure/AWS).
• Implement MLOps practices including CI/CD integration, experiment tracking, model registries, and monitoring.
• Ensure high‑quality 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.

• Deep hands‑on experience building and deploying ML models (supervised, unsupervised, deep learning, NLP).
• Strong Python skills (Num Py, Pandas, Scikit‑learn, PyTorch or Tensor Flow).
• Experience with SQL and data‑engineering 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 GCP) and scalable inference pipelines.
• Strong understanding of model monitoring, drift detection, and retraining strategies.
• Ability to create clear model explanations and apply interpretability tools.

• Experience in fintech or other regulated industries with compliance requirements (SOC 2, PCI‑DSS, FedRAMP)
• Background with Lang Chain, multi‑agent frameworks, or advanced vector search architectures.
• Experience implementing governance, safety guardrails, or Responsible AI frameworks.