Job Title: AI Ops and ML Ops Software Engineer (Mid-Level)

Incrivelsoft; United States; Remote; USD 110,000 - 170,000; Full time

Job summary

Incrivelsoft seeks a skilled AI Ops and ML Ops Software Engineer to design, implement, and manage production pipelines for AI/ML models. The role collaborates with data scientists, DevOps, and software engineers to ensure scalable and reliable deployments.

You will build CI/CD for models, monitor drift and performance, automate retraining and deployment, and optimize cloud infrastructure for cost efficiency. Applicants should have 3–5 years in AI/ML Ops and strong scripting and debugging skills.

Qualifications

• 3–5 years software engineering with AI/ML Ops focus.
• Proficient in Python, Java, or Go.
• Hands-on experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn.
• Experience with cloud platforms (AWS, Azure, GCP) and Docker/Kubernetes.
• Familiar with IaC tools such as Terraform or Ansible.
• Experience with data pipelines and orchestration tools (Airflow, Kubeflow, Prefect).
• Knowledge of model monitoring tools (Evidently AI, MLflow, SageMaker Monitor).
• Strong problem-solving and debugging skills.
• Understanding of CI/CD principles and Jenkins/GitHub Actions.

Responsibilities

• Build and manage CI/CD pipelines for deploying AI/ML models to production.
• Design and implement monitoring systems to track model performance, drift, and operational health.
• Automate model retraining, testing, and deployment processes.
• Collaborate with data scientists to ensure seamless integration of models into production systems.
• Optimize infrastructure for scalable and cost-effective machine learning operations.
• Troubleshoot and resolve issues in AI/ML pipelines and deployed systems.
• Implement robust version control and experiment tracking for models and datasets.
• Ensure compliance with security and data privacy regulations in model operations.

Skills

Python

Java

Go

ML deployment

Problem solving

Debugging

CI/CD concepts

Tools

Docker

Kubernetes

Terraform

Ansible

Airflow

Kubeflow

Prefect

MLflow

SageMaker Monitor

Job description

About the Role:

We are seeking a skilled and proactive AI Ops and ML Ops Software Engineer to join our team. This mid-level role is focused on designing, implementing, and managing operational pipelines and infrastructure for AI and machine learning models in production environments. You will work closely with data scientists, DevOps engineers, and software developers to ensure scalability, reliability, and efficiency in delivering AI-driven solutions.

Key Responsibilities:

• Build and manage CI/CD pipelines for deploying AI/ML models to production.
• Design and implement monitoring systems to track model performance, drift, and operational health.
• Automate model retraining, testing, and deployment processes.
• Collaborate with data scientists to ensure seamless integration of models into production systems.
• Optimize infrastructure for scalable and cost-effective machine learning operations.
• Troubleshoot and resolve issues in AI/ML pipelines and deployed systems.
• Implement robust version control and experiment tracking for models and datasets.
• Ensure compliance with security and data privacy regulations in model operations

Required Skills and Experience:

• 3–5 years of experience in software engineering, with a focus on AI/ML Ops or DevOps.
• Proficiency in programming languages like Python, Java, or Go.
• Hands‑on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Expertise in cloud platforms (e.g., AWS, Azure, GCP) and containerization tools like Docker and Kubernetes.
• Strong understanding of infrastructure as code (IaC) tools (e.g., Terraform, Ansible).
• Familiarity with data pipelines and orchestration tools (e.g., Airflow, Kubeflow, Prefect).
• Knowledge of model monitoring tools (e.g., Evidently AI, MLflow, SageMaker Monitor).
• Strong problem-solving and debugging skills.
• Understanding of CI/CD principles and tools (e.g., Jenkins, GitHub Actions).