AI Ops Engineer

AI-Ops Engineering

Qualifications & Skills:

Education:

• Bachelors or Masters degree in Computer Science, Data Engineering, AI, or a related field.
• Relevant certifications in cloud platforms (AWS, Azure, GCP) or MLOps frameworks are a plus.

Experience:

• 3+ years of experience in AI/ML operations, MLOps, or DevOps for AI-driven solutions.
• Hands-on experience deploying and managing AI models, including LLMs and GenAI solutions, in production environments.
• Experience working with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI.

Technical Skills:

• Proficiency in MLOps tools and frameworks such as MLflow, Kubeflow, or Airflow.
• Hands-on experience with monitoring tools (Prometheus, Grafana, ELK Stack) for AI performance tracking.
• Experience with containerization and orchestration tools (Docker, Kubernetes) to support AI workloads.
• Familiarity with automation scripting using Python, Bash, or PowerShell.
• Understanding of GenAI-specific operational challenges such as response monitoring, token management, and prompt optimization.
• Knowledge of CI/CD pipelines (Jenkins, GitHub Actions) for AI model deployment.
• Strong understanding of AI security principles, including data privacy and governance considerations.

Key Responsibilities:

AI Model Deployment & Integration:

• Deploy and manage AI/ML models, including traditional machine learning and GenAI solutions (e.g., LLMs, RAG systems).
• Implement automated CI/CD pipelines for seamless deployment and scaling of AI models.
• Ensure efficient model integration into existing enterprise applications and workflows in collaboration with AI Engineers.
• Optimize AI infrastructure for performance and cost efficiency in cloud environments (AWS, Azure, GCP).

Monitoring & Performance Management:

• Develop and implement monitoring solutions to track model performance, latency, drift, and cost metrics.
• Set up alerts and automated workflows to manage performance degradation and retraining triggers.
• Ensure responsible AI by monitoring for issues such as bias, hallucinations, and security vulnerabilities in GenAI outputs.
• Collaborate with Data Scientists to establish feedback loops for continuous model improvement.

Automation & MLOps Best Practices:

• Establish scalable MLOps practices to support the continuous deployment and maintenance of AI models.
• Automate model retraining, versioning, and rollback strategies to ensure reliability and compliance.
• Utilize infrastructure-as-code (Terraform, CloudFormation) to manage AI pipelines.

Security & Compliance:

• Implement security measures to prevent prompt injections, data leakage, and unauthorized model access.
• Work closely with compliance teams to ensure AI solutions adhere to privacy and regulatory standards (HIPAA, GDPR).
• Regularly audit AI pipelines for ethical AI practices and data governance.

Soft Skills:

• Strong problem-solving skills with the ability to troubleshoot complex AI operational issues.
• Excellent communication skills to effectively collaborate with cross-functional stakeholders.
• Proactive and results-driven mindset with a focus on operational efficiency and scalability.
• Ability to work effectively in a fast-paced, dynamic environment.