Machine Learning Architect

Job Title: Machine Learning Architect

Location: Pittsburgh, PA/ Dallas, TX

Job Summary:

Machine Learning Architect to lead the design, development, and deployment of scalable machine learning solutions. This individual will work closely with data scientists, engineers, product managers, and business stakeholders to build and implement state-of-the-art ML models that drive business value, enhance customer experience, and optimize internal operations.

Key Responsibilities:

• Architect end-to-end machine learning solutions from data ingestion to model deployment and monitoring.
• Design scalable, reliable, and secure ML systems integrated into business workflows and decision-making processes.
• Collaborate with data engineering and DevOps teams to deploy models in production environments using MLOps best practices.
• Develop reusable ML components, frameworks, and infrastructure.
• Guide data scientists on model development, performance evaluation, and feature engineering strategies.
• Ensure governance, model interpretability, and compliance with regulatory requirements (e.g., Fair Lending, GDPR).
• Lead experimentation to validate hypotheses and deliver insights.
• Maintain awareness of new tools, trends, and technologies in the AI/ML space.

Basic Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• 8+ years of experience in data science, machine learning, or AI development.
• 3+ years in a technical lead or architect role designing ML systems.

Preferred Skills and Experience:

• Strong understanding of supervised and unsupervised learning, deep learning, NLP, and recommendation systems.
• Experience with ML frameworks and tools such as TensorFlow, PyTorch, scikit-learn, XGBoost, Hugging Face.
• Proficient in Python and SQL; familiarity with Java or Scala is a plus.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and their ML toolkits (e.g., SageMaker, Vertex AI, Azure ML).
• Familiarity with MLOps concepts, CI/CD pipelines, and tools such as MLflow, Kubeflow, or Airflow.
• Knowledge of data privacy, ethics in AI, and model explainability techniques (e.g., SHAP, LIME).
• Excellent communication and stakeholder engagement skills.
• Experience in financial services or banking is highly desirable.