**Key Responsibilities**
- Design, implement, and automate ML lifecycle workflows using tools like **MLflow**, **Kubeflow**, **Airflow**and **OCI Data Science Pipelines**.
- Build and maintain **CI/CD pipelines**for model training, validation, and deployment using **GitHub Actions**, **Jenkins**, or **Argo Workflows**.
- Collaborate with data engineers to deploy models within **modern data lakehouse architectures**(e.g., **Apache Iceberg**, **Delta Lake**, **Apache Hudi**).
- Integrate machine learning frameworks such as **TensorFlow**, **PyTorch**, and **Scikit-learn**into distributed environments like **Apache Spark**, **Ray**, or **Dask**.
- Operationalize model tracking, versioning, and drift detection using **DVC**, model registries, and ML metadata stores.
- Manage **infrastructure as code (IaC)**using tools like **Terraform**, **Helm**, or **Ansible**to support dynamic GPU/CPU training clusters.
- Configure real-time and batch data ingestion and feature transformation pipelines using **Kafka**, **Goldengate**and **OCI Streaming**.
- Collaborate with DevOps and platform teams to implement robust **monitoring, observability**, and **alerting**with tools like **Prometheus**, **Grafana**, and the **ELK Stack**.
- Support **AI governance**by enabling model explainability, audit logging, and compliance mechanisms aligned with enterprise data and security policies.
**Required Qualifications**
- Bachelor’s or Master’s degree in **Computer Science**, **Data Science**, or a related technical discipline.
- **5-8 years**of experience in **ML engineering**, **DevOps**, or **data platform engineering**, with at least **2 years in MLOps**or model operations.
- Proficiency in **Python**, particularly for automation, data processing,



and ML model development.
- Solid experience with **SQL**and distributed query engines (e.g., **Trino**, **Spark SQL**).
- Deep expertise in **Docker**, **Kubernetes**, and cloud-native container orchestration tools (e.g., **OCI Container Engine**, **EKS**, **GKE**).
- Working knowledge of **open-source data lakehouse frameworks**and **data versioning**tools (e.g., **Delta Lake**, **Apache Iceberg**, **DVC**).
- Familiarity with model deployment strategies, including **batch**, **real-time inference**, and **edge deployments**.
- Experience with **CI/CD pipelines**(GitHub Actions, GitLab CI, Jenkins) and **MLOps frameworks**(Kubeflow, MLflow, Seldon Core).
- Strong understanding of **cloud platforms**(OCI, AWS, GCP) and **IaC tools**(Terraform, CloudFormation).
**Preferred Qualifications**
- Experience integrating AI workflows with **Oracle Data Lakehouse**, **Databricks**, or **Snowflake**.
- Hands-on experience with orchestration tools like **Apache Airflow**, **Prefect**, or **Dagster**.
- Exposure to **real-time ML systems**using **Kafka**or **Oracle Stream Analytics**.
- Understanding of **vector databases**(e.g., **Oracle 23ai Vector Search**).
- Knowledge of **AI governance**, including model explainability, auditability, and reproducibility frameworks.
**Soft Skills**
- Strong **problem-solving**skills and an automation-first mindset.
- Excellent **cross-functional communication**, especially when collaborating with data scientists, DevOps, and platform engineering teams.
- A collaborative and **knowledge-sharing**attitude, with good documentation habits.
- Passion for **continuous learning**, especially in the areas of AI/ML tooling, open-source platforms, and data engineering innovation.

📌 Senior Ai/aiops Engineer (México)
🏢 Oracle
📍 México