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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