AI Operations Engineer

Job Responsibilities

Years of Experience: 3-5 Yrs

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.

Collaboration & Process Improvement:

• Work closely with AI Engineers, Product Managers, and IT teams to align AI operational processes with business needs.

• Contribute to the development of AI Ops documentation, playbooks, and best practices.

• Continuously evaluate emerging GenAI operational tools and processes to drive innovation.

Skills/Qualifications:

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.

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.