Machine Learning Principal Solutions Architect
New
Based in the United StatesFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonSQLGCPJavaSnowflakeAzureSparkScalaDatabricks
Requirements
- 10+ years of experience as a Machine Learning Engineer, Software Engineer, Data Engineer, Data Scientist, or similar technical role.
- Expertise in programming languages such as Python, Scala, or Java.
- Experience developing APIs and web applications using frameworks such as Flask, Django, or Spring.
- Proven ability to design, build, and operate complex data pipelines using SQL and distributed query technologies.
- Hands-on experience with big data platforms like Spark, Snowflake, Databricks, Redshift, or Amazon EMR.
- Demonstrated experience deploying machine learning models into production environments.
- Strong understanding of cloud architecture, Linux-based systems, and modern compute platforms.
- Experience working directly with customers in consulting, professional services, or client-facing technology environments.
- Proven ability to manage pre-sales activities, project scoping, and technical discovery.
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Responsibilities
- Lead the end-to-end architecture, implementation, and lifecycle management of AI and machine learning solutions for strategic client engagements.
- Own solution design and delivery across the full ML lifecycle, including model development, deployment, monitoring, and production operations.
- Design and optimize environments that enable data scientists and engineers to build, train, test, and deploy machine learning models.
- Build and maintain robust data pipelines by integrating information from multiple sources.
- Define production infrastructure, deployment approaches, and operational strategies to ensure reliable consumption of AI solutions.
- Partner with data scientists to transform data into actionable insights and production-ready machine learning models.
- Serve as a trusted technical advisor to senior and executive stakeholders, guiding AI/ML roadmaps and strategic decisions.
- Lead architecture discussions, discovery workshops, and complex problem-solving sessions with clients.
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