Junior Solutions Architect - MLOps & Real-Time Data Integration

Striim, (pronounced “stream” with two i’s for integration and intelligence), is a unified data integration and streaming platform that connects clouds, data, and applications with unprecedented speed and simplicity to deliver the right data at the right time. Striim is used by enterprise companies to monitor events across any environment, build applications that drive digital transformation, and leverage true real-time analytics to provide a superior experience to their customers. At our company, we believe and expect all of our employees to operate as one with unlimited potential and dignity. We are seeking a Junior Solution Architect with a strong foundation in data science, MLOps, cloud data platforms, and modern data engineering to help design and implement real-time data integration and AI-enabled architectures. Working alongside experienced Solution Architects and Engineering teams, this role provides an opportunity for an early-career professional to gain hands-on experience designing scalable streaming data solutions that power enterprise AI, cloud modernization, and real-time analytics. The ideal candidate is eager to apply data science and machine learning concepts to real-world enterprise challenges, expand technical expertise across modern cloud and data technologies, and develop into a trusted technical architect within a collaborative, fast-paced environment. 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. 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…