Principal Machine Learning Solutions Architect

We are looking for a Principal Machine Learning Solutions Architect to join our Applied AI team. In this role, you will lead the architecture, implementation, and lifecycle management of AI/ML applications that deliver measurable business value for our clients. You will take full ownership of strategic AI/ML projects from vision and solution design through deployment and ongoing optimization, ensuring that models can be trained, tuned, and operated reliably using client data. You will collaborate closely with clients, Sales, data scientists, ML engineers, data engineers, platform/DevOps teams, and business stakeholders to deliver high-quality solutions and advance phData's delivery excellence.

Key Responsibilities:

• 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 that create clear business impact.

• Translate complex business and data science requirements into scalable, secure, and resilient AI/ML architectures, defining the environments, data flows, and infrastructure required for model development, training, tuning, and serving.

• Lead technical and strategic client engagements, including workshops, discovery sessions, and architecture reviews, to align stakeholders on AI/ML roadmaps, deployment approaches, and production-readiness standards.

• Ensure the quality, reliability, and observability of AI/ML solutions through robust testing strategies, documentation, monitoring, and governance that meet security, compliance, and performance expectations.

• Contribute to and leverage reusable assets such as reference architectures, accelerators, templates, and playbooks, while mentoring team members and partnering with Sales and account leadership to grow strategic AI/ML engagements.

About You:

You are a customer-obsessed technical leader and consultant who enjoys solving complex data and AI/ML challenges while building trusted relationships with clients. You are equally comfortable discussing architecture with executives and diving deep into code, infrastructure, and data pipelines with engineering teams. You thrive in an outcomes-driven environment, manage multiple work streams with ease, and bring a blend of strong engineering skills, strategic thinking, and excellent communication to every engagement. You are comfortable operating in distributed, global teams and partnering with colleagues across time zones.

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.

Technical / Functional Skills

• 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.

• Willingness to travel up to 40%, varies based on client needs.

Education:

• Bachelor’s degree in a relevant technical field (such as Computer Science) or equivalent practical experience.

Preferred Qualifications:

Preferred qualifications help candidates stand out but are not required for success in this role.

• 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.

Why phData?

• Impactful Work: Partner with leading organizations on meaningful data & AI initiatives.
• Collaborative Culture: Work with a supportive, high-performing global team that values transparency, autonomy, and continuous improvement.
• Growth Opportunities: Access to challenging projects, mentorship, and structured development pathways.
• Values-Driven: We prioritize doing the right thing for our clients, our teams, and our community.

Benefits at phData

US:

• 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)