Data Analyst

Our client is seeking a proactive and technically skilled Data Analyst(MLOps & Analytics Support Specialist)to join a dynamic team supporting an in-house AI platform that powers multiple national franchise brands. This role plays a critical part in ensuring the reliability and performance of AI applications by maintaining data pipelines, monitoring deployments, and supporting the infrastructure that enables advanced analytics and decision-making. The ideal candidate is a detail-oriented problem solver with hands-on experience in cloud environments, data engineering, and AI operations. You will thrive in this role if you bring a collaborative mindset, foundational MLOps capabilities, and a passion for supporting AI-driven tools that make a measurable business impact.

ESSENTIAL FUNCTION

Data Pipeline Operations & Support

• Monitor and manage ingestion and indexing pipelines in Microsoft Azure or similar cloud environments for both structured and unstructured data sources
• Troubleshoot issues related to data quality, metadata tagging, chunking, and embedding refreshes
• Maintain storage and indexing layers (e.g., SQL, data lake, vector databases) and assist with access control, health checks, and incident triage
• Support content and configuration updates to ensure timely and accurate data processing
• Exposure to graph databases such as Neo4j is a plus

AI Applications Supp

• Ensure data agents are connected properly for real-time KPI access and decision-making support
• Manage service accounts, API connections, and permissions for smooth integration with analytics, too
• Assist with application deployments using CI/CD pipelines and containerized services in Azure
• Monitor production environments and provide incident response or rollback support when needed

Model Operations and Tes

• Support deployment and monitoring of LLM applications, including containerization, version control, logging, and alerts
• Help maintain evaluation frameworks using golden datasets, regression testing, and canary releases to validate AI tool performance and reduce hallucinations
• Collaborate with the data team to ensure models are grounded in accurate, approved internal content

QUALIFICATIONS

Technical Proficiency

Mandatory: Strong proficiency in Python for scripting, automation, troubleshooting, and operational support

Mandatory: Experience deploying and operating AI/ML workloads in a cloud environment (Microsoft Azure preferred), including services such as Azure OpenAI Service or equivalent managed model endpoints.

Preferred: Experience supporting data stores commonly used in production AI systems (SQL databases, Fabric Lakehouse/data lake, and vector databases). Exposure to

graphdatabases (e.g., Neo4j) is a plus.

Preferred: Working knowledge of SQL for basic reporting, data validation, and KPIexploration. Experience supporting BI tools (e.g., Power BI).

Preferred: Foundational MLOps experience (e.g., CI/CD maintenance for ML/AIapplications, monitoring/alerting, environment management, and runbook/documentation ownership). Familiarity with Microsoft Fabric or similar data platforms is a plus.

AI/ML Proficiency

Working understanding of Retrieval-Augmented Generation (RAG) concepts, including embeddings, indexing, retrieval, and prompt grounding.

Familiarity with proprietary LLMs (e.g., GPT models) and exposure to deploying or fine-tuning open-source models (e.g., Llama, Mistral) is a plus.

Experience supporting and validating AI assistants/agents that utilize LLMs and external tools (APIs, databases, workflows) for practical business use cases.

Education & Experience

Bachelor's or master's degree in computer science, Data Science, Engineering, or related technical field.

1-2 years of experience in AI/ML, Data Engineering, Analytics, or MLOps/PlatformSupport role focused on operating and supporting production systems.

Experience supporting operations in the multi-brand business models is a significant plus