Job Title: Principal AI/ML Architect Data Science
Location: Onsite - Teaxs Locals
Experience: 12+ Years
Role Summary
We are seeking a highly experienced Principal AI/ML Architect with 12+ years of expertise in Data Science, Artificial Intelligence, Machine Learning, and Enterprise Analytics. The ideal candidate will lead the design, development, and deployment of scalable AI/ML solutions that drive business transformation. This role will be responsible for defining AI strategy, architecting enterprise-grade ML platforms, mentoring technical teams, and partnering with business stakeholders to deliver measurable outcomes through advanced analytics and intelligent automation.
Key Responsibilities
- Define and drive enterprise AI/ML architecture strategy aligned with business objectives.
- Design scalable machine learning, deep learning, and generative AI solutions for large-scale production environments.
- Lead end-to-end AI lifecycle including data ingestion, feature engineering, model development, deployment, monitoring, and governance.
- Architect cloud-native AI platforms leveraging AWS, Azure, or Google Cloud Platform services.
- Develop MLOps frameworks for continuous integration, deployment, monitoring, and model governance.
- Design and implement predictive, prescriptive, and generative AI solutions across multiple business domains.
- Lead architecture reviews, technical design sessions, and AI governance initiatives.
- Collaborate with Data Engineers, Data Scientists, Product Owners, and Executive Leadership to define AI roadmaps.
- Establish best practices for model explainability, fairness, security, compliance, and responsible AI.
- Mentor and guide teams on advanced AI/ML methodologies and emerging technologies.
Required Qualifications
- 12+ years of experience in Data Science, Machine Learning, Artificial Intelligence, and Enterprise Data Platforms.
- Strong expertise in Python, SQL, R, and advanced statistical modeling.
- Extensive experience with Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI technologies.
- Hands-on experience with LLMs, RAG architectures, AI Agents, Prompt Engineering, and Vector Databases.
- Expertise in MLOps frameworks including MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
- Strong knowledge of distributed computing frameworks such as Spark and Databricks.
- Experience designing cloud-native AI solutions on AWS, Azure, or Google Cloud Platform.
- Deep understanding of data architecture, data governance, and AI security principles.
- Experience leading enterprise-scale AI transformation programs.