AI Systems Engineer
United States
$200k - $275k/year
Full-time
Remote
1 month ago
About the role
Overview:
We are seeking a proactive AI Systems Engineer to design, implement, and maintain end-to-end AI systems that scale. You will bridge the gap between data science, software engineering, and operations, ensuring robust, reliable, and secure AI-enabled solutions that meet business objectives. The ideal candidate combines strong systems engineering discipline with hands-on experience in AI/ML model deployment, MLOps, and production-grade software.
Key Responsibilities:
Required Qualifications:
We are seeking a proactive AI Systems Engineer to design, implement, and maintain end-to-end AI systems that scale. You will bridge the gap between data science, software engineering, and operations, ensuring robust, reliable, and secure AI-enabled solutions that meet business objectives. The ideal candidate combines strong systems engineering discipline with hands-on experience in AI/ML model deployment, MLOps, and production-grade software.
Key Responsibilities:
- Design, deploy, and operate production-grade AI systems and pipelines (data ingestion, preprocessing, model training, validation, deployment, monitoring, and retraining).
- Collaborate with data scientists to translate research models into scalable, maintainable, and observable services.
- Implement MLOps practices: versioning for data, models, and code; CI/CD for ML pipelines; automated testing and canaries; model governance and drift monitoring.
- Build and maintain scalable data architectures (ETL/ELT, streaming, data lakes/warehouses) with emphasis on data quality, lineage, and observability.
- Develop APIs and services for model inference, including high-throughput, low-latency endpoints; ensure security, authentication, and access controls.
- Design and implement monitoring, alerting, and incident response for AI systems (model performance, data quality, system health, latency, cost).
- Optimize infrastructure for cost, performance, and reliability (cloud platforms, containers, orchestration, GPUs/accelerators, edge devices where applicable).
- Ensure compliance with privacy, security, and regulatory requirements; implement audit trails and reproducibility.
- Collaborate with product managers and stakeholders to define requirements, success metrics, and acceptance criteria. -Mentor junior engineers, contribute to standard methodologies, documentation, and best practices.
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Software Engineering, Electrical Engineering, Analytics, or related field (or equivalent practical experience).
- 3+ years of experience in systems engineering, ML/AI deployment, or MLOps.
- Strong software engineering skills: proficiency in one or more general-purpose languages (e.g., Python, Java, Go, C++) and familiarity with software engineering best practices (version control, testing, code reviews).
- Experience architecting and deploying end-to-end AI pipelines (data ingestion, feature engineering, model training, deployment, and monitoring).
- Hands-on experience with ML frameworks (TensorFlow, PyTorch, scikit-learn) and model serving platforms (TensorFlow Serving, TorchServe, MLflow, Kedro, Seldon, or similar).
- Proficiency with cloud platforms (AWS, Azure, GCP) and containerization (Docker), orchestration (Kubernetes), and CI/CD tooling.
Skills
Information Technology