Junior Solutions Architect - MLOps & Real-Time Data Integration

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.

• →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.

• 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.