MLOps Engineer

Position : MLOps Engineer

Type :

Contract (6–12 months, extension possible)

Location : Remote

Seniority : Intermediate

Department :

Machine Learning / AI Engineering

Role Summary

We are seeking an

Intermediate MLOps Engineer

to support the development, deployment, and monitoring of machine learning systems in production.

The ideal candidate has hands-on experience with

CI / CD pipelines

Docker

Kubernetes

MLflow

, and

data pipeline automation

, and enjoys bridging the gap between data science and infrastructure engineering.

You will work alongside data scientists and software engineers to ensure models are

reproducible, scalable, and observable

across cloud and hybrid environments.

Key Responsibilities

Model Deployment & Operations

• Build, deploy, and maintain

machine learning models

and APIs in production using

Docker

and

Kubernetes

CI / CD pipelines

to automate model testing, packaging, and deployment.

MLflow tracking and model registry

, ensuring version control, experiment logging, and performance monitoring.

Data Pipeline Development

data ingestion and transformation pipelines

using Python, Airflow, or similar orchestration tools.

ETL / ELT processes

and automate data quality checks to support model training and inference.

Infrastructure & Cloud Engineering

cloud environments

(AWS, GCP, or Azure).

Kubernetes

and monitor system health using tools like

Prometheus

Grafana

, or

Datadog

infrastructure-as-code

principles with

Terraform

or Helm for repeatable, scalable environment setup.

Monitoring & Optimization

model observability and drift detection

with MLflow and custom monitoring dashboards.

cost, latency, and reliability

Required Skills & Experience

of experience as an

MLOps Engineer

Data Engineer

, or

ML Engineer

with production exposure.

CI / CD tools

(GitHub Actions, Jenkins, GitLab CI, or Azure DevOps).

Docker

and

Kubernetes

for model packaging and orchestration.

MLflow

for experiment tracking, model management, and deployment.

Python

(Pandas, Pytest, Click, Pydantic, etc.) and familiarity with

Bash scripting

data pipeline orchestration

(Airflow, Prefect, or similar).

cloud services

(AWS S3, GCP Storage, Azure ML).

monitoring, logging, and alerting

frameworks for ML systems.

Nice-to-Have Skills

Kubeflow

Vertex AI

, or

AWS SageMaker

feature stores

vector databases

, or

RAG architectures

model governance

testing

, and

explainability frameworks

FinOps or performance optimization

practices for ML workloads.

Education

Computer Science, Data Engineering, or related fields

, or equivalent industry experience.