MLOps Engineer

Creuto; Khordha; Vor Ort; INR 1.200.000 - 1.800.000; Vollzeit

Zusammenfassung

Creuto in India is seeking an MLOps Engineer with 2–5 years of experience to build and maintain the infrastructure, pipelines, and tooling that move machine learning models from development to production with reliability.

You will collaborate with data scientists and AI engineers to reduce friction between model building and deployment, design CI/CD pipelines and model serving infra, data pipelines, and monitoring to ensure production readiness.

Qualifikationen

• 2 to 5 years of MLOps or ML infrastructure experience.
• Proficient in Python and comfortable with both ML frameworks and DevOps tooling.
• Hands-on experience with cloud ML platforms on AWS SageMaker, GCP Vertex AI, or Azure ML.
• Understand the full ML lifecycle and the reliability challenges of production ML systems.
• Work closely with data scientists while bringing engineering rigour to the infrastructure.

Aufgaben

• Build and maintain CI/CD pipelines for model training, evaluation, and deployment.
• Design and manage model serving infrastructure for low-latency, high-reliability inference at scale.
• Build data and feature pipelines that feed model training and inference systems reliably.
• Implement model monitoring systems to detect performance drift, data drift, and unexpected behaviours in production.
• Manage experiment tracking and model versioning using tools like MLflow or Weights and Biases.
• Containerise ML workloads using Docker and orchestrate them on Kubernetes or similar platforms.
• Optimise inference costs and latency through batching, quantisation, and hardware-aware deployments.

Kenntnisse

Python

ML frameworks

DevOps tooling

Cloud ML platforms

Tools

Docker

Kubernetes

Jobbeschreibung

DEPARTMENT

Artificial Intelligence

EMPLOYMENT TYPE

Full Time

LOCATION

India

EXPERIENCE

2 + Years

About the Role

We are looking for an MLOps Engineer with 2 to 5 years of experience to build and maintain the infrastructure, pipelines, and tooling that take machine learning models from development to production reliably. You will work closely with data scientists and AI engineers to remove the friction between model building and model deployment.

In This Role, You Will

• Build and maintain CI/CD pipelines for model training, evaluation, and deployment.
• Design and manage model serving infrastructure for low-latency, high-reliability inference at scale.
• Build data and feature pipelines that feed model training and inference systems reliably.
• Implement model monitoring systems to detect performance drift, data drift, and unexpected behaviours in production.
• Manage experiment tracking and model versioning using tools like MLflow or Weights and Biases.
• Containerise ML workloads using Docker and orchestrate them on Kubernetes or similar platforms.
• Optimise inference costs and latency through batching, quantisation, and hardware-aware deployments.
• Work with data scientists to ensure models are production-ready in terms of quality, reliability, and observability.

You Might Thrive in This Role If You

• Have 2 to 5 years of MLOps or ML infrastructure engineering experience.
• Are proficient in Python and comfortable with both ML frameworks and DevOps tooling.
• Have hands-on experience with cloud ML platforms on AWS SageMaker, GCP Vertex AI, or Azure ML.
• Understand the full ML lifecycle and the specific reliability challenges of production ML systems.
• Can work closely with data scientists while also bringing strong engineering rigour to the infrastructure.

Bonus If You Have

• Experience with LLM serving infrastructure and optimising transformer model inference.
• Familiarity with feature stores such as Feast or Tecton.
• Knowledge of distributed training frameworks such as Ray or Horovod.
• Experience with Kubeflow, Airflow, or Prefect for pipeline orchestration.