MLOps Engineer

Soul Ai; Bengaluru; Hybrid; INR 738,000 - 1,661,000; Part time

Job summary

Soul Ai is hiring a contract-based ML infrastructure engineer to architect and optimize end-to-end ML workflows. You will implement scalable pipelines using Kubeflow, MLflow, and SageMaker, and build robust CI/CD processes with GitHub Actions, Jenkins, and GitLab CI/CD.

The role emphasizes automation, model deployment, observability, and cost optimization across AWS, GCP, and Azure, with flexible remote/hybrid work options in India.

Qualifications

• Proficiency in Python, TensorFlow, PyTorch, CI/CD pipelines.
• Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
• Expertise in monitoring tools (MLflow, Prometheus, Grafana).
• Knowledge of distributed data processing (Spark, Kafka).
• Bonus: Experience in A/B testing, canary deployments, serverless ML.

Responsibilities

• Architect and optimize ML infrastructure with Kubeflow, MLflow, SageMaker Pipelines.
• Build CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI/CD).
• Automate ML workflows (feature engineering, retraining, deployment).
• Scale ML models with Docker, Kubernetes, Airflow.
• Ensure model observability, security, and cost optimization in cloud (AWS/GCP/Azure).

Skills

Python

TensorFlow

PyTorch

CI/CD

Tools

GitHub Actions

Jenkins

GitLab CI/CD

Docker

Kubernetes

Airflow

MLflow

Prometheus

Grafana

Spark

Kafka

Job description

Why Join?

Above market-standard compensation

Contract-based or freelance opportunities (212 months)

Work with industry leaders solving real AI challenges

Flexible work locations Remote | Onsite | Hyderabad/Bangalore

Your Role:

• Architect and optimize ML infrastructure with Kubeflow, MLflow, SageMaker Pipelines
• Build CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI/CD)
• Automate ML workflows (feature engineering, retraining, deployment)
• Scale ML models with Docker, Kubernetes, Airflow
• Ensure model observability, security, and cost optimization in cloud (AWS/GCP/Azure)

Must-Have Skills:

• Proficiency in Python, TensorFlow, PyTorch, CI/CD pipelines
• Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
• Expertise in monitoring tools (MLflow, Prometheus, Grafana)
• Knowledge of distributed data processing (Spark, Kafka)
• (Bonus: Experience in A/B testing, canary deployments, serverless ML)