AI Systems Engineer
Confidential · United States
- Hybrid
- Full-time
- $150,000 / year
- United States
Tailored resume — keyword-matched to this role.
Hiring manager — we find who's hiring.
Intro email — drafted to reach them directly.
Job highlights
- Design and deploy scalable AI systems.
- Implement robust MLOps practices.
- Build and maintain data architectures.
- Develop secure inference APIs.
- Optimize AI system performance and cost.
About the role
AI Systems Engineer
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.
Key skills/competency
- AI Systems Engineering
- MLOps
- Cloud Platforms (AWS, Azure, GCP)
- Containerization (Docker)
- Orchestration (Kubernetes)
- CI/CD
- Python
- Machine Learning Frameworks (TensorFlow, PyTorch)
- API Development
- System Monitoring
Skills & topics
- AI Systems Engineer
- MLOps
- Machine Learning
- Python
- Cloud Computing
- AWS
- Azure
- GCP
- Kubernetes
- Docker
- Data Engineering
- Software Engineering
- Systems Engineering
- Production Deployment
- Model Serving
How to get hired
- Tailor your resume: Highlight AI systems engineering, MLOps, and cloud platform experience.
- Showcase projects: Detail your experience with AI pipeline deployment and model serving.
- Prepare for technical interviews: Brush up on Python, cloud services, and Kubernetes.
- Understand the role: Emphasize your ability to bridge data science and engineering.
- Ask insightful questions: Inquire about team structure and AI strategy.
Technical preparation
Master Python for AI/ML development.,Deep dive into cloud AI services.,Practice Kubernetes and Docker.,Build end-to-end ML pipelines.
Behavioral questions
Describe a complex AI system you built.,How do you handle production AI issues?,Explain collaboration with data scientists.,How do you ensure AI system security?
Frequently asked questions
- What is the minimum required experience for the AI Systems Engineer role?
- The AI Systems Engineer role requires a minimum of 3+ years of experience in systems engineering, ML/AI deployment, or MLOps. A Bachelor's or Master's degree in a related field, or equivalent practical experience, is also necessary.
- What programming languages are essential for the AI Systems Engineer position?
- Proficiency in general-purpose languages like Python, Java, Go, or C++ is essential for the AI Systems Engineer role. Python is particularly highlighted for its use in AI/ML frameworks and general software engineering practices.
- What are the key responsibilities of an AI Systems Engineer at this company?
- Key responsibilities include designing, deploying, and operating AI systems and pipelines, implementing MLOps practices, building data architectures, developing inference APIs, and ensuring system monitoring and optimization.
- Which cloud platforms are relevant for the AI Systems Engineer job?
- Proficiency with major cloud platforms such as AWS, Azure, and GCP is required for the AI Systems Engineer position. Experience with containerization (Docker) and orchestration (Kubernetes) is also crucial.
- How can I best demonstrate my qualifications for the AI Systems Engineer role?
- To demonstrate your qualifications, tailor your resume to highlight your experience in AI systems engineering, MLOps, and cloud deployment. Be prepared to discuss your hands-on experience with ML frameworks and model serving platforms during the interview.
- What is the importance of MLOps in the AI Systems Engineer role?
- MLOps is critical for the AI Systems Engineer role as it involves implementing practices like versioning for data and models, CI/CD for ML pipelines, automated testing, model governance, and drift monitoring to ensure robust AI systems.
- Does the AI Systems Engineer role involve working with specific ML frameworks?
- Yes, hands-on experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn is a requirement for the AI Systems Engineer position. Familiarity with model serving platforms is also expected.