Sr. Machine Learning Solutions Architect
New
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonSQLGCPMachine LearningSnowflakeAzureSparkDatabricksMLOps
Requirements
- 8+ years of experience in machine learning engineering, data engineering, or software engineering with production-grade ML system delivery.
- Strong expertise in Python (or similar languages such as Scala or Java), including experience building APIs and backend services using frameworks like Flask or Django.
- Hands-on experience with distributed data processing and big data platforms such as Spark, Snowflake, Databricks, Redshift, or EMR.
- Strong knowledge of cloud and systems architecture across AWS, Azure, or GCP, including storage, networking, and data infrastructure design.
- Proven experience deploying machine learning models into production environments with robust monitoring, testing, and operational support.
- Advanced SQL skills, including query optimization and working with large-scale distributed datasets.
- Experience in consulting or client-facing delivery environments, with the ability to translate technical concepts into business value.
- Strong communication and leadership skills with the ability to guide cross-functional teams and influence technical direction.
Responsibilities
- Lead end-to-end architecture, design, and delivery of machine learning and data platform solutions for enterprise clients, ensuring scalability, security, and measurable business value.
- Translate business requirements and data science needs into production-ready MLOps architectures, including model training, deployment, monitoring, and retraining pipelines.
- Design and implement secure, scalable environments for data ingestion, transformation, and model development across cloud and hybrid ecosystems.
- Build and optimize data pipelines and distributed processing systems using modern big data technologies such as Spark, Snowflake, and Databricks.
- Define and enforce best practices for model lifecycle management, including CI/CD for ML, observability, testing strategies, and operational reliability.
- Collaborate closely with cross-functional teams and client stakeholders to guide technical strategy, architecture decisions, and solution roadmaps.
- Contribute to internal accelerators, reference architectures, and reusable frameworks that improve delivery efficiency and standardization across engagements.
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