AI Operations

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The AI Ops is responsible for establishing and managing an integrated platform to deploy, monitor, and scale Gen-AI, machine learning, and automation solutions across the organization. This role blends Gen-AI Ops with ML Ops and automation expertise to streamline generative AI models’ lifecycle, from development to production, and drive operational efficiency through automation.

Responsibilities

Key Responsibilities
• Gen-AI Operations (Gen-AI Ops)
• Configure and maintain infrastructure for deploying and managing generative AI models, ensuring seamless integration into business workflows.
• Implement model management processes to track generative AI performance, usage, and potential drift, ensuring models remain accurate, relevant, and ethical.
• Collaborate with data scientists and engineers to deploy generative AI models at scale, optimizing computational efficiency and resource allocation.
• Create guardrails for responsible Gen-AI usage, including monitoring for unintended biases, hallucinations, or ethical issues in generated outputs.
• ML Operations (ML Ops)
• Build and manage CI/CD pipelines for deploying machine learning models, incorporating monitoring and rollback capabilities to maintain model performance in production.
• Establish model governance processes, tracking model metadata, versioning, and auditing model usage for compliance.
• Work closely with the data science team to transition models from experimentation to production while ensuring alignment with operational standards.
• AI Factory Development and Management
• Design and manage a centralized AI Factory that supports generative AI, traditional ML, and automation, creating reusable components for consistent model deployment across the organization.
• Implement standardized processes for AI experimentation, deployment, and scaling, creating a seamless environment for rapid iteration and innovation.
• Ensure compliance with organizational policies and regulatory requirements for all AI initiatives, including Gen-AI governance.
• Facilitate cross-department collaboration to foster a unified approach to AI, aligning models with business objectives.
• Process Automation
• Identify and automate repetitive business processes across departments using RPA tools, scripting, or custom AI-driven solutions.
• Develop and deploy automation solutions to reduce manual workflows, improve efficiency, and free up resources for high-value tasks.
• Maintain and troubleshoot automation workflows, adapting them as business needs evolve.

Requirements

Soft Skill
• Emotional intelligence
• Strategic Thinking
• Collaboration
Technical Skills
• Customer Focus / Customer Centric
• Cloud Computing
• Cyber Security
• Partner/Vendor Management
• Dev Ops
• Machine Learning
• Leadership
• Communication
• Influencing & Relating
• Innovation and Agility
Education
• S1 (Strata 1) in Computer Sciences or Information Technology
Certifications
• AWS Certified Developer
Industry Experience
• Computer and Technology
Main Responsibilities
• Competencies
• Infrastructure as Code (IaC)
• Data Science & Machine Learning
• Years