MLOps Engineer

Soul Ai; Bengaluru; Hybrid; INR 2,500,000 - 4,200,000; Part time

Benefits offered by this job

Remote/onsite options

Job summary

Soul Ai in Bengaluru seeks an experienced ML infrastructure engineer to architect scalable ML pipelines and production-ready platforms. You will implement Kubeflow, MLflow, and SageMaker Pipelines, and own end-to-end deployment automation across cloud environments.

Join a team solving real AI challenges with flexible remote/onsite options and contract-based opportunities across Hyderabad and Bengaluru, collaborating with data scientists and ML engineers to operationalize research.

Qualifications

• Proficient in Python and ML/DL frameworks (TensorFlow, PyTorch).
• Experience building and maintaining CI/CD pipelines for ML workloads.
• Hands-on with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
• Familiar with monitoring/observability tools (MLflow, Prometheus, Grafana).
• Knowledge of distributed data processing (Spark, Kafka).

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

Distributed data processing concepts

Tools

Kubeflow

MLflow

SageMaker Pipelines

Docker

Kubernetes

Airflow

GitHub Actions

Jenkins

GitLab CI/CD

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)