Principal Applied AI Solutions Architect
New
P
phDataData and AI
Listing locations: USAFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience as a Machine Learning Engineer, Software Engineer, Data Engineer, or Data Scientist building and deploying production data and machine learning solutions.
- Required Skills
- AWSPythonSQLSnowflakeSparkRESTful APIsLinuxDatabricks
Requirements
- Have 10+ years of experience as a Machine Learning Engineer, Software Engineer, Data Engineer, or Data Scientist building and deploying production data and machine learning solutions.
- Be proficient in a modern programming language such as Python or similar for production-grade data and ML solutions.
- Have experience designing and integrating APIs and services that expose ML models.
- Build and operate robust data pipelines across diverse data sources and toolsets.
- Have strong working knowledge of SQL and experience writing, debugging, and optimizing complex and distributed queries.
- Have hands-on experience with big data and analytics platforms such as Spark, Snowflake, Databricks, Redshift, Amazon EMR, or HDFS.
- Be familiar with data source systems such as JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP.
- Have systems-level knowledge of network and cloud architecture, Linux-based operating systems, and storage and compute platforms.
- Have experience designing and operating production ML systems for performance, security, scalability, and reliability.
- Have end-to-end software development lifecycle experience for data and ML solutions, including model deployment, monitoring, and lifecycle management.
- A relevant technical bachelor's degree or equivalent practical experience is listed as optional.
Responsibilities
- Own and drive end-to-end solution design and delivery of AI/ML and data solutions for strategic client accounts.
- Ensure reliable model deployment, retraining, monitoring, and production operations.
- Translate business and data science requirements into scalable, secure, and resilient AI/ML architectures.
- Lead client workshops, discovery sessions, and architecture reviews to align stakeholders on roadmaps and production-readiness standards.
- Ensure solution quality, reliability, and observability through testing, documentation, monitoring, and governance.
- Contribute reusable reference architectures, accelerators, templates, and playbooks.
- Mentor team members and partner with Sales and account leadership on strategic AI/ML engagements.
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