AI Operations

Description

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. The position also focuses on implementing a governance framework to ensure ethical, compliant, and strategic usage of AI technologies within the organization.

Key Responsibilities:

1. 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.
2. 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.
3. 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.
4. 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.

Soft Skill

Emotional intelligenceStrategic ThinkingCollaboration

Technical Skills

Customer Focus / Customer CentricCloud ComputingCyber SecurityPartner/Vendor ManagementDev OpsMachine Learning

Leadership

CommunicationInfluencing & RelatingInnovation 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 of Experience

3-5 Years