Senior Software Engineer AI/ML Platform

Socure • Carson City, NV

5d

About The Position

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. This role is ideal for someone who enjoys building platforms and tools that abstract complexity for ML and data science teams, and who thrives in fast-paced environments where engineering excellence and reliability are paramount.

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; familiarity with Go/Rust is a plus.
• 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 Haves

• 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.

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees