Job Description
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.
