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Junior Solutions Architect - MLOps & Real-Time Data Integration

4 weeks ago
United States
Full-time
Junior
Machine Learning Engineer
Software Development
AWS Azure CI/CD Databricks Docker Feature Engineering GCP Git Java JSON Kubeflow Kubernetes Machine Learning MLflow MLOps Python REST API Snowflake SQL Vertex AI
Apply Now
Striim

Striim

Striim is a cutting-edge platform for real-time data integration and streaming, empowering businesses to process trillions of events instantly and make informed decisions with operational intelligence.

IT Services
51-250
Founded 2012
$108M raised
View All Jobs 3

Description

  • 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 degree in Computer Science, Data Science, Software Engineering, Information Systems, or a related technical discipline.

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, and public holidays).
  • Commission-based rewards.
  • Opportunity to contribute to and shape an upbeat, fully engaged culture.

Interested in this position?

Apply directly on the company website

Apply Now

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