Data Engineer – Agentic AI & ML Ops (Co-op)
Reads like a ML Engineer role: putting models into production.
USA - NJ - Camden
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingDevelop and support ETL/ELT pipelines and AI/ML workflows
Build and test AI agents and intelligent workflows
Ingest, transform, and orchestrate data using Python, PySpark, and SQL
Troubleshoot pipeline failures, performance issues, and data quality gaps
Create runbooks, workflows, and technical documentation
From the employer’s posting
Develop and support ETL/ELT pipelines and AI/ML workflows.
Build and test AI agents and intelligent workflows for automation and data access.
Ingest, transform, and orchestrate data using Python, PySpark, and SQL.
Troubleshoot pipeline failures, performance issues, and data quality gaps.
Create runbooks, workflows, and technical documentation.
What you’ll bring
All qualificationsCore experience
- Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs.
- Strong communication and teamwork skills.
Preferred experience
- Familiarity with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, or Git is a plus.
- Experience with Python-based AI/ML projects or notebooks is a plus.
Qualification wording
Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs.
Strong communication and teamwork skills.
Familiarity with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, or Git is a plus.
Experience with Python-based AI/ML projects or notebooks is a plus.
Tools in this posting
- Python
- SQL
- Azure
- Databricks
- Power BI
- Snowflake
- PySpark
Source — Tool mentions in context
- 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.
- 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.
- Pursuing a degree in Computer Science, Data Engineering, Data Science, AI/ML, or related field. - Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs. - 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. - Strong analytical and problem-solving skills.
Learning & Development Opportunities - Hands-on experience with Databricks, Snowflake, Azure, ADLS, ADF, and Power BI. - Exposure to MLOps, GenAI, Agentic AI, LLMs, CI/CD, and automation.
- Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs. - 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.
Posting record
Report an errorOpen 4 monthsWe first saw this listing 129 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 15, 2026 · 129 days ago
- Recorded sightings
- 269
- Last seen by us
- Oct 22, 2026
- Employer says posted
- Jun 12, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
Since 1869, we've connected people through food they love. We’re proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell’s brand, as well as Cape Cod, Chunky, Goldfish, Kettle Brand, Lance, Late July, Pacific Foods, Pepperidge Farm, Prego, Pace, Rao’s Homemade, Snack Factory, Snyder’s of Hanover. Swanson, and V8.
Here, you will make a difference every day. You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us.
Operational Support Data Engineer – Agentic AI & ML Ops (Co-op)
- 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.
Key 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.
Learning & Development Opportunities
- 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.
Qualifications
- Pursuing a degree in Computer Science, Data Engineering, Data Science, AI/ML, or related field.
- Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs.
- 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.
- Strong analytical and problem-solving skills.
- Strong communication and teamwork skills.
The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.
Source details
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
USA - NJ - Camden
Working pattern and location restrictions need checking in the full posting.
- Work authorization
- Strong communication and teamwork skills. The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.
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