Data Engineer – Agentic AI & ML Ops (Co-op)

Campbell's • Camden, NJ

1d

About The Position

We are seeking a motivated and curious Data Engineer – Agentic AI & ML Ops to join our Enterprise Data & Analytics team. This co-op provides hands-on experience supporting cloud-based data platforms, AI/ML operations, Generative AI, and Agentic AI solutions. You will work with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, Python, PySpark, SQL, LLMs, and modern AI/ML frameworks in an Agile environment. If you are passionate about data engineering, AI, and automation, we want to hear from you.

Requirements

• Pursuing a degree in Computer Science, Data Engineering, Data Science, AI/ML, or related field.
• Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs.
• Strong analytical and problem-solving skills.
• Strong communication and teamwork skills.

Nice To Haves

• Familiarity with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, or Git is a plus.
• Exposure to LLMs, GenAI, Agentic AI, MLOps, or CI/CD is beneficial.
• Experience with Python-based AI/ML projects or notebooks is a plus.

Responsibilities

• Build Data & AI Pipelines: Develop and support ETL/ELT pipelines and AI/ML workflows.
• Data Integration & Transformation: Ingest, transform, and orchestrate data using Python, PySpark, and SQL.
• Develop Agentic AI Solutions: Build and test AI agents and intelligent workflows for automation and data access.
• LLM & Prompt Engineering: Design and optimize prompts and workflows using LLMs and GenAI frameworks.
• AI/ML Development & Automation: Build Python-based scripts, APIs, and notebooks on cloud platforms.
• Support Analytics & AI/ML: Prepare datasets for reporting, ML models, forecasting, and advanced analytics.
• Monitor & Support Operations: Troubleshoot pipeline failures, performance issues, and data quality gaps.
• MLOps & CI/CD: Support deployment, testing, and automation for data and AI solutions.
• Data Modeling & Semantic Layers: Assist with STTM, data modeling, and reporting datasets.
• Agile Collaboration: Participate in sprint planning, stand-ups, and retrospectives.
• Documentation & Automation: Create runbooks, workflows, and technical documentation.

Benefits

• Hands-on experience with Databricks, Snowflake, Azure, ADLS, ADF, and Power BI.
• Exposure to MLOps, GenAI, Agentic AI, LLMs, CI/CD, and automation.
• Experience working with AI agents, prompt engineering, and workflow orchestration.
• Mentorship from Data, AI/ML, and Platform Engineers.
• Experience in Agile/Scrum and DevOps/MLOps environments.

What This Job Offers

• Career Level: Entry Level
• Education Level: No Education Listed