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About the Role
As a Machine Learning Engineer at Enfuce, you will build and maintain the infrastructure, tooling, and platforms that enable machine learning and generative AI solutions to be developed, deployed, and operated reliably at scale. You will own the production lifecycle of ML systems, from data pipelines and experiment tracking to model deployment, monitoring, and continuous delivery, helping establish MLOps best practices by building reproducible workflows, scalable infrastructure, and automation.
Responsibilities
- Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications.
- Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models.
- Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices.
- Build and maintain workflow orchestration, feature engineering, and data processing pipelines.
- Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health.
- Manage the end-to-end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability.
- Containerize ML workloads with Docker and deploy scalable services using cloud-native technologies and orchestration platforms.
- Develop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources.
- Collaborate with Data Scientists and software engineers to productionize, optimize, and scale machine learning solutions.
- Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.
Requirements
- Strong Python programming skills and proficiency with SQL.
- Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management.
- Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker.
- Strong understanding of the end-to-end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance.
- Experience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation).
- Experience with Docker, containerized ML workloads, and container orchestration platforms such as Kubernetes.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability.
- Familiarity with feature stores, model registries, artifact repositories, and modern MLOps practices.
- Experience deploying LLM or Generative AI applications is a strong advantage, along with excellent problem-solving, communication, and collaboration skills.
Skills
- Python
- SQL
- MLflow
- Snowflake
- dbt
- Snowpark ML
- Vertex AI
- Amazon SageMaker
- Git
- Infrastructure as Code
- Terraform
- CloudFormation
- Docker
- Kubernetes
- AWS
- Azure
- Google Cloud Platform
- LLM
- Generative AI
Location
- Remote
Work Type
- Remote
- Hybrid
Education Level
- Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, or a related field.
Salary/Compensations
- Competitive salaries reassessed regularly
- Employee stock option program
Benefits
- High autonomy & ownership
- Unlimited growth potential
- Work from anywhere up to 30 days
- Supportive culture
- Comprehensive benefits package
- Fair pay
- Employee stock option
- Flexible Paid Time Off
- Up to 5 weeks of annual vacation days
- Paid family leave
- Hybrid or remote work options
- Team activity budget
- Company-wide event
About the Company
- Enfuce is a company that values innovation and provides a supportive culture.