MLOps Engineer (Freelancer)

Soul Ai; Hyderabad; Hybrid; INR 1,200,000 - 1,800,000; Part time

Benefits offered by this job

Remote option

Flexible locations Hyderabad/Bangalore

Above market compensation

Job summary

Soul AI invites an experienced MLOps Engineer to architect scalable ML infrastructure and CI/CD pipelines across cloud platforms. You will optimize Kubeflow, MLflow, SageMaker Pipelines and ensure robust observability and cost efficiency.

You will work in a flexible setup with remote, onsite, or Hyderabad/Bangalore locations, taking ownership of deployments, monitoring, and security for production ML workloads.

Qualifications

• Experience with MLOps and ML infrastructure.
• Hands-on experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
• Knowledge of monitoring tools (MLflow, Prometheus, Grafana).
• Experience with 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 pipelines

AWS SageMaker

GCP Vertex AI

Azure ML

MLflow

Prometheus

Grafana

Spark

Kafka

A/B testing

Canary deployments

Serverless ML

Tools

Docker

Kubernetes

Airflow

Job description

Are you an MLOps Engineer ready to take your expertise to the next levelSoul 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-HaveSkills:

• 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)