Job Description

Technical Expertise and Required Qualifications

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). Bachelor's or Master's degree in

Computer Science

,

Data Science

, or a related technical discipline.

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.

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.