Job Description

We are seeking a motivated Model Integration Trainee to support the development and integration of AI/ML models, data pipelines, and workflow automation systems. This role is ideal for someone looking to begin their career in AI integration, RAG workflows, API connectivity, and enterprise system automation.

You will work under senior developers and architects to learn how AI-driven systems connect with platforms like ServiceNow, vector databases, and Databricks environments.

• Assist in building basic AI workflow components under guidance (e.g., data parsing, classification, routing logic).

• Support senior engineers in implementing Retrieval-Augmented Generation (RAG) pipelines using vector search, embeddings, and retrieval mechanisms.

• Help test and validate model output, classifications, and logic flows.

• Learn how to connect AI models to enterprise platforms using APIs, such as ServiceNow REST APIs.

• Support simple tasks like testing API requests, checking logs, and validating ticket creation/responses.

• Document integration steps and help maintain workflow diagrams.

• Assist in managing datasets, Delta tables, and workspace organization within Databricks.

• Support model deployment tasks by preparing files, environments, or test inputs.

• Help monitor pipeline performance and maintain basic data quality checks.

• Maintain documentation for workflows, integration steps, and test cases.

• Participate in meetings with the project lead and developers, taking notes and helping track tasks.

• Follow coding standards and contribute to organized, maintainable project files.

• Interest in AI model integration, data workflows, and enterprise automation.

• Basic Python knowledge (or willingness to learn quickly).

• Curiosity about APIs, NLP, vector databases, and LLM technologies.

• Strong analytical and problem-solving attitude.

• Good communication and eagerness to work with senior engineers.

• Exposure to NLP or LLM concepts

• Basic understanding of REST APIs

• Familiarity with Databricks, cloud platforms, or vector databases

• Understanding of RAG concepts or embeddings

• Experience with Python libraries (NumPy, Pandas, LangChain etc.)

• How real-world RAG systems are built and integrated

• How AI agents communicate with tools like ServiceNow

• Basics of model deployment, GPU-based training, and ML lifecycle

• Data flow and pipeline management in Databricks

• SLA logic, ticket automation, and request routing

• Industry-best practices in security and enterprise AI integration

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