Principal Applied AI Solutions Architect
RemoteUnited States
Job Summary
Own and drive end-to-end solution design and delivery of AI/ML and data solutions for strategic client accounts, ensuring reliable model deployment, retraining, monitoring, and production operations. Translate complex business and data science requirements into scalable, secure, and resilient AI/ML architectures, defining environments and data flows for model development and serving. Lead technical and strategic client engagements, including workshops, discovery sessions, and architecture reviews, to align stakeholders on roadmaps and deployment approaches. Ensure solution quality through robust testing, documentation, and governance while leveraging reusable assets to mentor team members and grow strategic engagements.
Required Qualifications
- 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 (or similar) for building production-grade data and ML solutions, including experience designing and integrating APIs and services that expose ML models
- Ability to build and operate robust data pipelines across diverse data sources and toolsets, with 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, HDFS, or similar large-scale data processing and storage technologies
- Familiarity with multiple data source systems such as JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP, and how they integrate into analytical and ML environments
- Systems-level knowledge of network and cloud architecture, Linux-based operating systems, and storage/compute platforms (e.g., AWS, Databricks, Cloudera), with proven experience designing and operating production ML systems for performance, security, scalability, and reliability
- End-to-end software development lifecycle experience (design, documentation, implementation, testing, deployment, and ongoing operations) for data and ML solutions, including model deployment, monitoring, and lifecycle management
- Bachelor's degree in a relevant technical field (such as Computer Science) or equivalent practical experience
Desired Qualifications
- Experience with cloud and data ecosystem technologies such as Spark, Databricks, Snowflake, AWS, Azure, or GCP in the context of building and operating AI/ML solutions
- Experience working with data science and machine learning libraries and frameworks such as H2O, TensorFlow, Keras, scikit-learn, or similar
- Experience with containerization and orchestration technologies such as Docker and Kubernetes, and with MLOps tooling such as AWS SageMaker, Azure ML, and MLflow for enterprise-scale ML
- Background in consulting or professional services, including pre-sales, project scoping, and strategic advisory work for data and AI/ML initiatives
- Contributions to technical communities, open source projects, public speaking, writing, or other relevant side projects demonstrating thought leadership in data and AI/ML
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