MLOps Engineer (Freelancer)

Soul Ai; Kolkata District; Remote; INR 1,378,000 - 2,480,000; Full time

Job summary

Soul AI is seeking an experienced MLOps Engineer to join its expert community. The role offers contract-based or freelance opportunities with flexible work locations including Remote, Onsite, and Hyderabad/Bangalore.

You will architect and optimize ML infrastructure, build robust CI/CD pipelines, automate ML workflows, scale models with container orchestration, and ensure observability, security, and cost efficiency across major cloud platforms.

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).
• BonusExperience 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 pipelines

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).