Systems Consultant

Role Overview

The Lead – Product Operations is responsible for ensuring that DRPL’s AI-driven video understanding and video modification systems run smoothly across both VoD and Live environments. This is a hands-on, highly technical role that blends dataset preparation, AI model training support, software testing, production monitoring, and DevOps practices . The role requires someone who can bridge the gap between engineering and operations, ensuring the health of our AI pipelines and infrastructure on a daily basis.

You will own operational excellence for production systems, ensuring data readiness, pipeline reliability, and proactive issue resolution across both cloud and on-premise (edge) deployments.

Key Responsibilities

AI Data & Training Operations

• Prepare, curate, and validate datasets for AI model training.

• Coordinate with AI engineers to ensure model training pipelines run efficiently.

• Validate AI model outputs against quality benchmarks before deployment.

Production Pipeline Monitoring & Maintenance

• Ensure end-to-end health of AI pipelines (data ingestion, processing, inference, output delivery).

• Monitor system uptime and performance using observability tools.

• Configure alerts and escalation workflows for critical failures or performance degradation.

• Maintain deployment documentation, runbooks, and standard operating procedures.

DevOps & Deployment

• Deploy and maintain AI and software components on cloud platforms as well as bare-metal/edge systems .

• Install and configure operating systems, dependencies, and runtime environments.

• Automate deployment and monitoring where possible.

Software & Model Testing

• Conduct integration and functional testing for new releases of AI models and platform features.

• Work with QA engineers to create and maintain test plans for AI workflows.

Cross-functional Collaboration

• Work closely with Engineering Leads and Architects to implement operational improvements.

• Coordinate with Product Managers to align operational tasks with business priorities.

Required Skills & Experience

• Strong technical background with hands-on experience in Linux systems administration , shell scripting, and automation tools.

• Knowledge of DevOps tools (Docker, Kubernetes, CI/CD systems, monitoring tools like Prometheus/Grafana).

• Understanding of AI/ML workflows including dataset preparation, training pipelines, and inference systems.

• Experience with cloud services (AWS, GCP, Azure) and bare-metal / edge deployments .

• Familiarity with software testing practices and QA methodologies.

• Ability to troubleshoot software, model, and system issues across the stack.

• Strong analytical and problem-solving skills; proactive in identifying and resolving operational issues.

• Comfortable working with both structured and unstructured data .

Nice-to-Have Skills

• Exposure to video processing or AI-based media workflows.

• Experience with GPU-accelerated systems and drivers.

• Familiarity with database systems and message queues (PostgreSQL, MongoDB, Kafka, Redis, etc.).

• Scripting in Python for automation and data manipulation.