Striim, Inc. - Junior Solutions Architect - MLOps & Real-Time Data Integration
Requirements
• 1–3 years of professional experience or equivalent graduate research, internships, or project experience in data science, machine learning, data engineering, cloud engineering, or solution architecture. • Strong foundation in data science, including machine learning algorithms, model selection, feature engineering, and the machine learning lifecycle. • Understanding of modern MLOps practices, including model deployment, inference, monitoring, versioning, and CI/CD for machine learning applications. • Experience or academic exposure to machine learning frameworks and platforms such as MLflow, Kubeflow, Vertex AI, SageMaker, or Azure Machine Learning. • Familiarity with modern data integration concepts, including Change Data Capture (CDC), event-driven architectures, and real-time streaming data pipelines. • Working knowledge of relational and NoSQL databases, including SQL proficiency and database administration fundamentals. • Experience with cloud platforms and modern cloud data ecosystems, including AWS, Azure, GCP, Databricks, Snowflake, BigQuery, Amazon Redshift, or Azure Synapse. • Experience programming in Python or Java and working with REST APIs and JSON. • Understanding of Docker containers and modern DevOps concepts; familiarity with Kubernetes, Git, and CI/CD pipelines. • Strong analytical, troubleshooting, written, and verbal communication skills. • Demonstrated curiosity, adaptability, and a passion for learning emerging technologies in AI, cloud computing, and real-time data streaming. • Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, Information Systems, or a related technical discipline.
Responsibilities
• Design and implement scalable real-time data integration and Change Data Capture (CDC) solutions using the Striim platform. • Design streaming data architectures connecting enterprise databases, cloud data platforms, messaging systems, and AI/ML environments. • Develop data pipelines that support machine learning workflows, feature engineering, model inference, and real-time AI applications. • Build proof-of-concepts, reference architectures, and deployment patterns for enterprise implementations. • Configure, optimize, and troubleshoot data pipelines across cloud and hybrid environments. • Collaborate with Engineering, Product, and GTM Engineering teams to validate architectural designs, resolve complex technical challenges, and improve platform capabilities. • Participate in architecture reviews, implementation planning, and production readiness activities. • Create technical documentation, architecture diagrams, and implementation best practices. • Evaluate emerging technologies across AI, MLOps, cloud computing, and real-time data streaming.
Benefits
• Competitive salary and pre-IPO stock options • Comprehensive health care plans (medical, dental and vision), including medical and dependent FSA • Paid Time Off (Vacation, Sick & Public Holidays) • The chance to contribute to and shape an upbeat, fully engaged culture • $120,000 - $130,000 USD on an annualized basis. In addition to base pay, this role offers the opportunity to earn commission-based rewards. • Applications will be reviewed on a rolling basis and accepted until the position is filled. • Our company culture fosters entrepreneurship and nurtures our team members to grow with the company. Come join a Silicon Valley startup focused on delivering a product that’s loved by its customers and primed to be a core part of the cloud data stack.
Apply in one click
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT