MLOps Engineer (Freelancer)

Soul Ai; Gurugram District; Hybrid; INR 1,653,000 - 2,755,000; Full time

Benefits offered by this job

Above-market compensation

Job summary

Soul AI invites you to join its Expert Community and work on advanced ML platforms across the stack. You will architect and optimize ML infrastructure, automate workflows, and scale models with modern tooling.

The role offers contract-based or freelance opportunities with remote, onsite options in India (Hyderabad/Bangalore) and above-market compensation.

Qualifications

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

Education

Bachelor's degree in a relevant field

Tools

Kubeflow

MLflow

SageMaker Pipelines

GitHub Actions

Jenkins

GitLab CI/CD

Docker

Kubernetes

Airflow

Job description

AI Opportunities with Soul AIs Expert Community!

Are you an MLOps Engineer ready to take your expertise to the next level? Soul AI (by Deccan AI) is building an elite network of AI professionals, connecting top-tier talent with cutting-edge projects.

Why Join.

Above market-standard compensation.

Contract-based or freelance opportunities (2\"“12 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).
• (BonusExperience in A/B testing, canary deployments, serverless ML).