AI Engineer
Integral Creative Solutions
- Location
- Location not stated
- Workplace
- Remote
- Employment
- Full Time
- Salary
- USD 200,000–275,000/yr
Posted 2mo ago
The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.
Responsibilities
- Design, deploy, and operate production-grade AI systems and pipelines
- Translate research models into scalable, maintainable, and observable services
- Implement MLOps practices
- Build and maintain scalable data architectures
- Develop APIs and services for model inference
- Design and implement monitoring, alerting, and incident response for AI systems
- Optimize infrastructure for cost, performance, and reliability
- Ensure compliance with privacy, security, and regulatory requirements
- Collaborate with product managers and stakeholders to define requirements
- Mentor junior engineers and contribute to standard methodologies
Requirements
- 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
- Proficiency in Python, Java, Go, or C++
- Familiarity with software engineering best practices (version control, testing, code reviews)
- Experience architecting and deploying end-to-end AI pipelines
- Hands-on experience with ML frameworks (TensorFlow, PyTorch, scikit-learn)
- Experience with model serving platforms (TensorFlow Serving, TorchServe, MLflow, Kedro, Seldon, or similar)
- Proficiency with cloud platforms (AWS, Azure, GCP)
- Proficiency with containerization (Docker)
- Proficiency with orchestration (Kubernetes)
- Proficiency with CI/CD tooling
- Strong understanding of data engineering concepts (ETL/ELT, data governance, data quality, lineage)
- Experience with model monitoring and drift detection
- Experience with A/B testing and experimentation pipelines
- Familiarity with security and compliance practices (IAM, secrets management, encryption, audit logging)
Preferred
- Master’s or PhD in a relevant field; specialization in ML systems, MLOps, or data engineering
- Experience with real-time inference
- Experience with streaming data (Kafka, Kinesis)
- Experience with feature stores
- Knowledge of DevOps fundamentals
- Knowledge of SRE practices
- Knowledge of reliability engineering for AI systems
- Experience with edge AI deployments or on-device inference
- Publications or contributions to open-source ML systems projects
Skills
- Python
- Java
- Go
- C++
- TensorFlow
- PyTorch
- scikit-learn
- TensorFlow Serving
- TorchServe
- MLflow
- Kedro
- Seldon
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- Kafka
- Kinesis
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