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Socure Logo Socure · IT - Software Development

Senior Software Engineer – AI/ML Platform

United States 180000.00 - 200000.00 USD / Year · Job Posted February 20, 2026
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Job Description

As a Senior Software Engineer on Socure’s AI Platform team, you’ll design and build infrastructure that supports model training, validation, deployment, and serving at scale. You will work with modern AWS-native technologies, focusing on low-latency microservices, automated pipelines, and robust deployment workflows to enable safe and efficient delivery of machine learning models into production.

Job Responsibility

  • Build and maintain scalable systems and infrastructure for deploying and serving ML models
  • Design low-latency, fault-tolerant model inference systems using Amazon SageMaker
  • Implement safe deployment strategies like blue/green deployments and rollbacks
  • Create and manage CI/CD pipelines for ML workflows
  • Monitor model performance and system health using AWS observability tools (e.g., CloudWatch)
  • Develop internal tools and APIs to help ML teams deploy and monitor models easily
  • Collaborate with ML engineers, data scientists, and DevOps to productionize new models
  • Participate in code reviews, system design, and platform roadmap discussions
  • Continuously improve deployment reliability, speed, and usability of the ML platform

Requirements

  • 4+ years of experience as a software engineer, with at least 2 years focused on low latency and highly available backend systems
  • Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field with a strong academic record
  • Strong fundamentals in data structures, algorithms, and distributed computing principles
  • Strong analytical and problem-solving skills, with a passion for AI and machine learning
  • Strong programming skills in Python
  • Hands-on experience with model systems including low latency model serving, registry, and pipeline orchestration(preferably SageMaker)
  • Solid understanding of MLOps best practices, including model versioning, testing, deployment, and reproducibility
  • Experience building and maintaining CI/CD pipelines for ML workflows
  • Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
  • Experience with database technologies (SQL, NoSQL, or data warehouses like Snowflake or Redshift)

Nice to have

  • Familiarity with Go/Rust
  • Experience building internal ML platform services or self-service tooling for model deployment and monitoring
  • Understanding of model optimization techniques (e.g., TorchScript, ONNX, quantization, batching)
  • Experience with feature stores, real-time feature serving, or caching systems for ML workloads
  • Background in deploying ML models into high-availability, mission-critical environments

What we offer

  • Offers Equity
  • Offers Bonus
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