**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). * Competence in implementing monitoring and logging systems (e.g., **Prometheus** , **ELK Stack** , **Datadog** ) for ML applications. * 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.