Senior AI/ ML Software Engineer

We are seeking a Senior Software Engineer to join our AI Platform team, where you'll architect and build core infrastructure that powers model training, validation, deployment, and high‑scale serving. In this role, you'll work with modern AWS-native systems, focusing on low‑latency microservices, automated ML pipelines, and resilient deployment workflows that enable fast, safe delivery of machine learning models into production.

This is an ideal opportunity for an engineer who enjoys building platforms and tools that simplify complex ML workflows, and who thrives in a fast‑moving environment where engineering excellence, performance, and reliability matter.

What You'll Do

• 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 such as blue/green rollouts, shadow deployments, and rollback mechanisms.
• Develop CI/CD pipelines tailored for ML workflows and automated model promotion.
• Monitor model performance and system health using AWS observability tools (e.g., CloudWatch, X-Ray).
• Build internal tools, APIs, and self‑service interfaces to empower ML and data science teams.
• Collaborate cross‑functionally with ML engineers, data scientists, and DevOps to productionize and scale new models.
• Participate in code reviews, architecture discussions, and platform roadmap planning.
• Continuously improve deployment reliability, speed, developer experience, and platform usability.

What You Bring

• 4+ years of experience as a software engineer, including 2+ years working on low‑latency, highly available backend systems.
• Bachelor's or Master's in Computer Science, Data Science, AI/ML, or a related technical field.
• Strong fundamentals in data structures, algorithms, and distributed computing.
• Proficiency in Python; experience with Go or Rust is a plus.
• Hands‑on experience with ML model systems including inference services, model registries, and orchestration pipelines (preferably with SageMaker).
• Solid understanding of MLOps best practices: model versioning, testing, deployment, reproducibility, and monitoring.
• Experience building and maintaining CI/CD pipelines for ML workflows.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, and Scikit‑learn.
• Experience with SQL or NoSQL databases, or cloud data warehouses (e.g., Redshift, Snowflake).

Preferred Qualifications

• Experience building internal ML platform services or self‑service tooling.
• Knowledge of model optimization: ONNX, TorchScript, quantization, batching, etc.
• Experience with feature stores or real‑time feature serving systems.
• Background deploying ML models into mission‑critical, high‑availability production environments.