Enterprise AI Architect
Role details
Job location
Tech stack
Job description
- Design comprehensive AI/ML architectures covering data ingestion, feature engineering, model development, deployment, monitoring, and retraining.
- Define and standardize enterprise AI reference architectures, platforms, frameworks, and tools.
- Partner with business, data, and engineering leaders to develop technical AI solutions.
- Establish reusable AI patterns, accelerators, and best practices to guide teams from proof-of-concept to production.
- Collaborate with data architecture teams to ensure data quality, lineage, and governance for AI workloads.
- Integrate AI solutions with enterprise data platforms, APIs, and modern data stacks.
- Define and enforce Responsible AI principles, including fairness, explainability, and bias mitigation.
- Design and implement MLOps/LLMOps frameworks for CI/CD, versioning, and monitoring.
- Provide architectural leadership and mentorship to data scientists and ML engineers.
Requirements
Education: A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field is required.
Experience: A minimum of 8 years in data, analytics, or platform architecture roles is necessary, including at least 4 years designing and implementing AI/ML solutions at scale. Proven experience deploying AI models into production environments is also required.
Technical Skills: Candidates must have strong knowledge of Machine Learning, Deep Learning, and Generative AI concepts, along with proficiency in Python and SQL. Experience with cloud AI platforms (Azure, AWS, Google Cloud Platform), MLOps tools, and data engineering platforms like DataStage or Spark is required. An understanding of APIs, microservices, and containers is also necessary., * Experience with Large Language Models (LLMs), prompt engineering, vector databases, and RAG architectures.
- Experience in financial services or other regulated industries.
- Certifications in cloud architecture, AI/ML, or data platforms.
- Experience building AI Centers of Excellence or AI enablement programs.