phData
Machine Learning Principal Solutions Architect
US-Remote · Principal
Sponsorship not specifiedDetected 1 day ago
PythonJavaScalaDjangoFlaskCode ReviewSQLMySQLSnowflakeRedshiftDatabricksAWSGCPAzureDockerLinuxDevOpsKafkaMachine LearningTensorFlowscikit-learnSparkdbtData Engineering
About the role
- Join phData, a remote-first data and AI consultancy company with employees across the United States, Latin America, and India.
- We're growing fast, and we give our people real ownership over their work.
- You are equally comfortable discussing architecture with executives and diving deep into code, infrastructure, and data pipelines with engineering teams.
Responsibilities
- Own and drive end-to-end solution design and delivery of AI/ML and data solutions for strategic client accounts, from model inference, retraining, and monitoring through to production operations.
- Design and create environments for data scientists to build, train, test, and tune AI/ML models and applications using relevant client data.
- Work within customer systems to extract data from a variety of sources and place it within analytical environments to support model development, training, and tuning.
- Define deployment approaches and production infrastructure for AI/ML models and applications, ensuring that businesses can reliably consume and maintain the solutions we deliver.
- Create and execute operational testing strategies, including QA validation, performance testing, and implementation plans to support testing and deployment of AI/ML solutions.
- Collaborate with cross-functional partners including data scientists, ML engineers, data engineers, platform/DevOps, and business stakeholders to deliver successful client engagements.
- Provide technical and strategic leadership during workshops, discovery sessions, architecture and design reviews, and project delivery.
- Partner closely with Sales and account leadership to drive account expansion, identify new opportunities, and ensure long-term client value on strategic accounts.
- Lead multiple work streams concurrently, ensuring alignment across technical teams, business stakeholders, and account leadership.
- 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.
Requirements
- Hands-on experience with big data and analytics ecosystem technologies such as Spark, Snowflake, Databricks, Redshift, Amazon EMR, HDFS, or similar platforms.
- Familiarity with multiple data source systems such as JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP.
- Systems-level knowledge of network and cloud architecture, Linux-based operating systems, and storage/compute platforms (e.g., AWS, Databricks, Cloudera).
- Proven Account Growth / Revenue Generation experience for external clients
- Experience delivering projects for external or internal clients in a professional services, product, or consulting environment.
- Strong written and verbal communication skills in English, with the ability to present technical concepts to both technical and non-technical audiences.
- Demonstrated ability to work effectively with distributed and cross-functional teams, including Sales, data scientists, ML engineers, data engineers, and business stakeholders.
- Proven track record of taking ownership of client outcomes, managing multiple priorities and work streams, and delivering high-quality work with minimal supervision.
Nice to have
- A Master's or other advanced degree in data science, computer science, or a related field.
- Hands-on experience with cloud and data ecosystem technologies such as Spark, Databricks, Snowflake, AWS, Azure, or GCP.
- Experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Experience with MLOps tooling such as AWS SageMaker, Azure ML, and MLflow, and with building enterprise-scale ML models.
- Prior experience in a consulting role or working closely with clients on strategic data and AI/ML initiatives.
- Relevant side projects such as contributions to open source technology stacks, technical communities, speaking, or writing.
- This role is based in the United States and operates primarily in the Central Time Zone.
- Some flexibility may be required to collaborate
Benefits
- Demonstrate the business value of data by partnering with data scientists to manipulate and transform data into actionable insights and deployable machine learning models.
This listing is sourced directly from phData's careers page and normalized into a canonical job model.