AI MLOps Engineer
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Job Location:
Monthly Salary:
Posted on:
3 hours ago
Vacancies:
1 Vacancy
Job Summary
3 5 years relevant industry experience in MLOps ML engineering or DevOps
with AI workloads
with AI workloads
Key Responsibilities
Design build and maintain endtoend MLOps pipelines for model training
validation deployment and monitoring
Productionize ML and AI models using CI/CD InfrastructureasCode and
container orchestration best practices
Implement model registries experiment tracking feature management and data
lineage to ensure reproducibility and governance
Deploy and operate realtime batch and APIbased inference services including
LLMpowered solutions where applicable
Monitor model performance drift bias latency and reliability; define SLAs
alerts and automated retraining workflows
Operationalize machine learning and AI models into production environments
using CI/CD best practices
Collaborate with data scientists to transition models from experimentation to
scalable enterprisegrade solutions
Implement model versioning data lineage and experiment tracking to ensure
reproducibility and governance
Monitor model performance drift bias and reliability; implement automated
alerts and retraining workflows
Develop and maintain infrastructure for ML workloads using cloud and
containerized platforms
Ensure AI solutions meet Fluors security data privacy and compliance
standards
Support AI use cases across EPC lifecycle phases including engineering
construction project controls HSE and operations
Document MLOps standards architecture and operating procedures for
enterprise reuse
Continuously evaluate new MLOps tools and technologies to improve efficiency
and scalability
alerts and retraining workflows
Develop and maintain infrastructure for ML workloads using cloud and
containerized platforms
Ensure AI solutions meet Fluors security data privacy and compliance
standards
Support AI use cases across EPC lifecycle phases including engineering
construction project controls HSE and operations
Document MLOps standards architecture and operating procedures for
enterprise reuse
Continuously evaluate new MLOps tools and technologies to improve efficiency
and scalability
Required Qualifications
Bachelors degree in Computer Science Engineering Data Science or a related
discipline
3 5 years of handson experience in MLOps DevOps or production ML
environments
Strong experience deploying and supporting ML models in production
Proficiency in Python and ML frameworks (e.g. TensorFlow PyTorch Scikitlearn)
Experience with CI/CD pipelines and automation tools
Solid understanding of software engineering best practices and system design
Education & Certifications
Bachelors degree required
Azure Cloud certifications related to ML or DevOps are a plus
Bachelors degree required
Azure Cloud certifications related to ML or DevOps are a plus
