Sr. Machine Learning Solutions Architect
New
P
phDataData & AI
Location: US-Remote, Central Time ZoneFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 8+ years
- Required Skills
- PythonSQLJavaMachine LearningSnowflakeSparkScalaDatabricksMLOps
Requirements
- 8+ years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer building and deploying production data and machine learning solutions.
- Hands-on expertise in modern programming languages such as Python, Scala, or Java.
- Experience developing APIs and web applications using frameworks such as Flask, Django, or Spring.
- Experience building and operating robust data pipelines and distributed data processing solutions using SQL and big data technologies (e.g., Spark, Snowflake, Databricks, Redshift, Amazon EMR).
- Systems-level knowledge of network and cloud architecture, Linux-based operating systems, and data/storage platforms.
- Strong working knowledge of SQL and ability to write, debug, and optimize complex and distributed queries.
- Complete software development lifecycle experience, including design, documentation, implementation, testing, deployment, and operations.
- Excellent communication and presentation skills with experience working directly with customers.
- Experience delivering projects for external or internal clients in a professional services or consulting environment.
- Strong written and verbal communication skills in English.
Responsibilities
- Own and drive end-to-end architecture, solution design, and delivery of machine learning and data solutions for enterprise clients across diverse industries.
- Translate business and data science requirements into scalable technical and MLOps solutions that align with phData methodologies, standards, and best practices.
- Design and create secure, scalable environments and tooling for data scientists to build, train, and manipulate models and data.
- Define deployment approaches and production infrastructure for machine learning models, ensuring that businesses can reliably use, monitor, and maintain the models we develop.
- Create and execute operational testing strategies, including QA validation, performance testing, and implementation plans, to support model testing and deployment.
- Provide technical and strategic leadership during workshops, discovery sessions, architecture and design reviews, and project delivery.
- Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery, and standardize patterns for deploying and operating ML solutions.
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