Principal Applied AI Solutions Architect
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Job Description
Principal AI/ML architect leading enterprise data and machine-learning solutions for phData, a remote-first data and AI consultancy. Designing production architectures, deployments, monitoring, and client delivery across strategic engagements.
Responsibilities:
- Lead the architecture, implementation, and lifecycle management of AI/ML applications that deliver measurable business value
- Own strategic AI/ML projects from vision and solution design through deployment and ongoing optimization
- 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
- Define environments, data flows, and infrastructure for model development, training, tuning, and serving
- Lead client workshops, discovery sessions, and architecture reviews
- Align stakeholders on AI/ML roadmaps, deployment approaches, and production-readiness standards
- Ensure solution quality, reliability, observability, testing, documentation, monitoring, and governance
- Contribute to reusable reference architectures, accelerators, templates, and playbooks
- Mentor team members and partner with Sales and account leadership to grow strategic AI/ML engagements
- Collaborate with clients, Sales, data scientists, ML engineers, data engineers, platform/DevOps teams, and business stakeholders
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 (or similar)
- 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
- Strong working knowledge of SQL, including writing, debugging, and optimizing complex distributed queries
- Hands-on experience with big data and analytics platforms such as Spark, Snowflake, Databricks, Redshift, Amazon EMR, HDFS, or similar technologies
- Familiarity with JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP and their integration into analytical and ML environments
- Systems-level knowledge of network and cloud architecture, Linux-based operating systems, and storage/compute platforms such as AWS, Databricks, and Cloudera
- Proven experience designing and operating production ML systems for performance, security, scalability, and reliability
- End-to-end software development lifecycle experience for data and ML solutions, including design, documentation, implementation, testing, deployment, ongoing operations, model deployment, monitoring, and lifecycle management
- Bachelor’s degree in a relevant technical field such as Computer Science or equivalent practical experience is listed under “Education - If desired” and is not required
- Preferred: experience with Spark, Databricks, Snowflake, AWS, Azure, or GCP for AI/ML solutions
- Preferred: experience with H2O, TensorFlow, Keras, scikit-learn, or similar ML frameworks
- Preferred: experience with Docker, Kubernetes, AWS SageMaker, Azure ML, and MLflow
- Preferred: consulting or professional services background, including pre-sales, project scoping, and strategic advisory work
- Preferred: technical community, open source, public speaking, writing, or thought leadership contributions
Benefits:
- Remote-First Work Environment
- 401k plan with company match
- Dental and Vision insurance
- Home Office Equipment Stipend
- Annual stipend for Learning and Development
- Competitive comp, excellent benefits, 4 weeks PTO plan plus 10 Holidays (and other cool perks)
- Access to challenging projects, mentorship, and structured development pathways
- Supportive, high-performing global team
- Transparency, autonomy, and continuous improvement
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