AI Solutions Engineer

AI Solutions Engineer

About the Role

As an AI Solutions Engineer, you'll be a core member of our engineering team, responsible for the end-to-end development and operationalization of our AI Network Management Tool. This involves designing, implementing, and maintaining scalable data pipelines, integrating with various network systems and cloud services, and deploying sophisticated AI models. You'll work with large datasets, modern cloud technologies, and contribute directly to a platform that leverages Retrieval Augmented Generation (RAG) to provide intelligent insights and automation for network incidents.

What You'll Do

• Design & Develop Scalable AI Solutions:

  Build and optimize the core architecture and components of the AI Network Management Tool on Microsoft Azure, focusing on data ingestion, processing, indexing, and AI model integration.

• Data Pipeline Engineering:

  Create robust and efficient data pipelines to collect, transform, and store network data and incident information from diverse sources.

• Database Management:

  Design, implement, and manage highly available and performant database solutions (both relational and NoSQL) for storing processed data and analytics.

• Azure Cloud Infrastructure:

  Provision, configure, and manage Azure cloud resources (e.g., Azure Functions, Azure Event Hubs, Azure Data Lake Storage, Azure Blob Storage, Azure AI Search, Azure OpenAI Service) using Infrastructure-as-Code (IaC) principles.

• DevOps & MLOps:

  Implement and maintain CI/CD pipelines for automated testing, deployment, and monitoring of AI models and application components within an Azure DevOps environment.

• AI Model Integration:

  Integrate and fine-tune Large Language Models (LLMs) from Azure OpenAI Service, including embedding models for vectorization and advanced LLMs (like GPT-4/GPT-4o) for incident analysis, natural language understanding, and response generation within a RAG framework.

• Problem Solving & Optimization:

  Troubleshoot and resolve complex technical issues, identify performance bottlenecks, and continuously optimize the platform for efficiency, scalability, and cost-effectiveness.

What You'll Bring

• 5+ years of experience

  in software engineering or platform engineering, with a strong focus on building and deploying scalable solutions.

• Expert-level proficiency in Python

  for data manipulation, automation, and API integration.

• Strong experience with database technologies

  , including both relational databases (e.g., Azure SQL Database) and NoSQL databases (e.g., Azure Cosmos DB).

• Hands-on experience with Microsoft Azure Cloud services

  , including but not limited to Azure Functions, Azure Event Hubs, Azure Storage (Blob/Data Lake), Azure AI Search, and Azure OpenAI Service.

• Solid understanding and practical experience with DevOps principles and tools

  , particularly Azure DevOps (pipelines, repos) and Git for version control.
• Proven ability to work with large, complex datasets and unstructured text.
• Excellent problem-solving, analytical, and communication skills.

Bonus Points

• Networking knowledge will be an added advantage.

  Experience with network protocols, device configurations (e.g., Cisco, Aruba), network monitoring tools (e.g., Splunk), or network automation concepts.
• Familiarity with Microsoft Copilot Studio or other chatbot development platforms.
• Experience with data visualization tools like Power BI.