DevOps Engineer AI Ops

Job Title :

-
Dev Ops Engineer with AI Ops Experience

Employment Type

:

- W2

Duration :

- Long Term

Visa Type :

- All Visa applicable which are ready for W2

Location
- Atlanta, GA (Day-1 Onsite)

Exp required :

- 5+ years of experience in Dev Ops engineering, with at least 3 years specializing in AI Ops or supporting ML/AI model deployment and infrastructure.

• 5+ years of experience in Dev Ops engineering, with at least 3 years specializing in AI Ops or supporting ML/AI model deployment and infrastructure.
• Proven experience in designing, implementing, and managing CI/CD pipelines and ML Ops frameworks to automate AI/ML workflows.

• Proficiency in cloud platforms (AWS, GCP, Azure) with hands-on experience in deploying AI/ML models and utilizing AI/ML services (e.g., AWS Sage Maker, Google AI Platform).
• Strong skills in containerization and orchestration tools such as Docker and Kubernetes, especially for deploying machine learning models at scale.
• Experience with infrastructure-as-code tools like Terraform, Cloud Formation, or Ansible to manage and provision cloud and on-premise environments.
• Proficiency in CI/CD tools (e.g., Jenkins, Git Lab CI, Circle
  
  CI) to build automated pipelines for AI/ML model training, testing, and deployment.
• Solid understanding of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) for model performance tracking and infrastructure observability.
• Strong programming and scripting skills in Python, Bash, and YAML for automating workflows and integrating services.

• Experience with MLOps best practices, including model versioning, automated retraining, and model governance for reliable and reproducible AI pipelines.
• Hands-on experience with model monitoring tools (e.g., MLflow, Kubeflow, or TFX) to track model performance, drift, and retraining needs.
• Familiarity with data pipelines and orchestration tools (e.g., Apache Airflow, Prefect) for managing data and model workflows.
• Knowledge of model deployment strategies (e.g., blue-green deployments, canary releases) to ensure reliable AI/ML model deployment with minimal downtime.
• Experience with A/B testing and experiment tracking to evaluate model performance in production and measure the impact on business KPIs.

• Ability to design and manage scalable infrastructure to support machine learning workloads, ensuring cost efficiency, performance, and security.
• Proficiency in automating testing and deployment processes for data and model pipelines to support fast, reliable releases.
• Familiarity with serverless architectures and cloud-native tools for AI, allowing for flexible and efficient resource management.
• Experience with security best practices, including role-based access control, data encryption, and compliance requirements for data-sensitive applications.

• Excellent communication skills with the ability to collaborate closely with data scientists, ML engineers, and software development teams.
• Proven ability to document infrastructure, CI/CD pipelines, and MLOps processes, ensuring transparency and knowledge sharing across teams.
• Strong problem-solving skills and a proactive approach to troubleshooting, particularly in managing and resolving deployment and performance issues.
• Ability to train and mentor team members on MLOps tools, best practices, and model deployment techniques.

• Experience with data security and governance standards, especially related to machine learning applications in regulated industries.
• Familiarity with AI ethics and compliance, including model fairness, transparency, and risk management.
• Knowledge of advanced monitoring and alerting tools and techniques to ensure the reliability of AI systems in production.
• Strong interest in staying up-to-date on the latest advancements in MLOps and AI Ops to continuously improve infrastructure and processes.