MLOps Engineer (Freelancer)

Soul Ai; Dadri; Hybrid; INR 1,240,000 - 2,066,000; Full time

Benefits offered by this job

Above market compensation

Job summary

Soul AI invites experienced MLOps engineers to join its expert community. You will architect and optimize ML infrastructure, implement robust CI/CD pipelines, and automate ML workflows across cloud platforms.

Role involves scaling models with Docker and Kubernetes, using Kubeflow and Airflow, and ensuring security, cost efficiency, and observability in AWS/GCP/Azure.

Qualifications

• Proficiency in Python, TensorFlow, PyTorch, and 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, and 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, and Airflow.
• Ensure observability, security, and cost optimization in cloud (AWS/GCP/Azure).

Skills

Python

TensorFlow

PyTorch

CI/CD pipelines

Tools

AWS SageMaker

GCP Vertex AI

Azure ML

MLflow

Prometheus

Grafana

Spark

Kafka

Kubeflow

MLflow Pipelines

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).