Principal Applied AI Solutions Architect
New
P
phDataData and AI Consulting
Employees across the United States, Latin America, and India, partnering with colleagues across time zonesFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonSQLMachine LearningSnowflakeDatabricksMLOps
Requirements
- 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.
- Strong proficiency in a modern programming language such as Python for building production-grade data and ML solutions.
- Experience designing and integrating APIs and services that expose ML models.
- Ability to build and operate robust data pipelines across diverse data sources.
- Strong working knowledge of SQL and experience writing, debugging, and optimizing complex and distributed queries.
- Hands-on experience with big data and analytics platforms such as Spark, Snowflake, Databricks, Redshift, Amazon EMR, or HDFS.
- Familiarity with multiple data source systems like Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP.
- Systems-level knowledge of network and cloud architecture, Linux-based OS, and storage/compute platforms.
- Proven experience designing and operating production ML systems for performance, security, scalability, and reliability.
- End-to-end software development lifecycle experience including model deployment, monitoring, and lifecycle management.
- Bachelor’s degree in a technical field such as Computer Science or equivalent practical experience.
Responsibilities
- Own and drive end-to-end solution design and delivery of AI/ML and data solutions for strategic client accounts.
- Translate complex business and data science requirements into scalable, secure, and resilient AI/ML architectures.
- Lead technical and strategic client engagements, including workshops, discovery sessions, and architecture reviews.
- Ensure the quality, reliability, and observability of AI/ML solutions through robust testing, documentation, monitoring, and governance.
- Contribute to and leverage reusable assets such as reference architectures and accelerators.
- Mentor team members and partner with Sales and account leadership to grow strategic AI/ML engagements.
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