Job description

Job Title :- DevOps 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 DevOps engineering, with at least 3 years specializing in AI Ops or supporting ML/AI model deployment and infrastructure. Job Description: • 5+ years of experience in DevOps 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. Technical Skills: • Proficiency in cloud platforms (AWS, GCP, Azure) with hands-on experience in deploying AI/ML models and utilizing AI/ML services (e.g., AWS SageMaker, 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, CloudFormation, or Ansible to manage and provision cloud and on-premise environments. • Proficiency in CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) 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. AI Ops and MLOps Skills: • 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. DevOps and Automation Skills: • 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. Communication and Collaboration Skills: • 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. Additional Qualifications: • 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.

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