Principal Applied AI Solutions Architect

phData

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.

Posted 8/5/2026full-timeRemote • 🇺🇸 United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the architecture and implementation of AI/ML applications, ensuring reliable model deployment and optimization. Proficient in designing scalable and secure AI/ML architectures while collaborating with cross-functional teams to deliver strategic solutions.

Highest-signal resume keywords

AI/ML Application ArchitecturePython ProgrammingSQL ProficiencyBig Data Platforms (Spark, Snowflake, Databricks)Production ML Systems Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringData Pipeline DevelopmentAPI Design and IntegrationModel Deployment and MonitoringEnd-to-End Software Development Lifecycle

Soft Skills

Stakeholder AlignmentMentoringCollaboration

Tools & Technologies

AWSAzureGCPDockerKubernetes

Industry Keywords

Data EngineeringData ScienceAnalyticsCloud ArchitectureConsulting

Tech Stack

Tools & technologies

Amazon RedshiftAWSAzureCloudDockerGoogle Cloud PlatformHDFSKafkaKerasKubernetesLinuxMySQLOpen SourceOraclePythonRDBMSScikit-LearnSparkSQLTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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