Job Description

We are seeking a motivated Model Integration Trainee to support the development, integration, and testing of AI/ML models, data workflows, and enterprise automation pipelines. This role is ideal for freshers or early-career professionals interested in AI integration, RAG workflows, APIs, and data engineering .
You will work closely with senior engineers and architects to learn how AI-driven systems integrate with platforms like ServiceNow, Databricks, and vector databases .

• Assist in building basic components of AI workflows such as data parsing, classification, and routing logic .

• Support retrieval-based systems including vector search, embeddings, and RAG pipelines .

• Perform model testing, output validation, and logic flow verification.

• Learn and contribute to integrating AI models with enterprise systems using REST APIs .

• Support tasks such as sending API test calls, validating system responses, and checking logs.

• Document integration steps and assist in maintaining workflow diagrams.

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

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

• Monitor pipeline performance and conduct basic data quality checks.

• Create and maintain documentation for workflows, test cases, and integration activities.

• Participate in team meetings, take notes, and help with task tracking.

• Follow coding and documentation standards for clean, maintainable work.

• Strong interest in AI model integration, automation, and data workflows .

• Basic knowledge of Python or willingness to learn quickly.

• Curiosity about APIs, NLP, vector databases, and LLM-based systems.

• Strong analytical skills and a problem-solving mindset.

• Good communication skills and eagerness to learn from senior engineers.

• Understanding of NLP or LLM fundamentals.

• Basic familiarity with REST APIs.

• Exposure to Databricks, cloud platforms, or vector databases.

• Awareness of RAG concepts and embeddings.

• Experience with Python libraries like NumPy, Pandas, or LangChain.

• Building and integrating real-world RAG and AI systems .

• How AI agents interact with enterprise tools (e.g., ServiceNow).

• Basics of model deployment and ML lifecycle management.

• Data engineering fundamentals in Databricks .

• Ticket automations, SLA logic, and enterprise workflow routing.

• Security best practices for enterprise AI systems.

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