2 дня назад
Data Engineer (Agentic AI & ML Ops)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Engineer (Agentic AI & ML Ops): Building and supporting cloud-based data engineering, AI/ML pipelines, and agentic AI workflows with an accent on Python, PySpark, SQL, Databricks, Snowflake, and Azure. Focus on developing AI agents, optimizing LLM prompts, automating MLOps and CI/CD workflows, and troubleshooting pipeline and data quality issues.
Location: Camden, New Jersey, USA; full-time onsite role
Company
A food and beverage company managing Campbell’s and a portfolio of established consumer food brands.
What you will do
- Build and support ETL/ELT data pipelines and AI/ML workflows.
- Ingest, transform, and orchestrate data using Python, PySpark, and SQL.
- Develop and test AI agents, intelligent workflows, LLM prompts, and generative AI solutions.
- Create Python scripts, APIs, notebooks, and automation on cloud platforms.
- Prepare datasets for reporting, forecasting, machine learning models, and advanced analytics.
- Monitor pipeline operations, troubleshoot performance and data quality issues, and support MLOps, testing, CI/CD, documentation, and Agile delivery.
Requirements
- Pursuing a degree in Computer Science, Data Engineering, Data Science, AI/ML, or a related field.
- Knowledge of SQL, Python or PySpark, ETL/ELT, and APIs.
- Strong analytical, problem-solving, communication, and teamwork skills.
- Ability to participate in an Agile/Scrum environment.
Nice to have
- Familiarity with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, or Git.
- Exposure to LLMs, generative AI, agentic AI, MLOps, CI/CD, or workflow orchestration.
- Experience with Python-based AI/ML projects or notebooks.
Culture & Benefits
- Hands-on experience with cloud data, AI/ML, MLOps, generative AI, and automation technologies.
- Mentorship from Data, AI/ML, and Platform Engineers.
- Practical experience in Agile/Scrum and DevOps/MLOps environments.
- Opportunities to build technical skills and contribute to enterprise data and analytics solutions.
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