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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Data Engineer II - **Company:** LA Care Health Plan - **Location:** Los Angeles, CA, United States - **Experience:** Experienced - **Salary:** $105,267.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Continuous Delivery, Continuous Integration, Information Engineering, Data Governance, DevOps, Python (Programming Language), Machine Learning, Meta-Data Management, Natural Language Processing, Performance Tuning, Scrum Methodology, Recommender Systems, Azure Machine Learning, Sentiment Analysis, SQL Databases, Data Streaming, Data Processing, Cloud Platform System, Feature Engineering, Data Ingestion, Informatica Powercenter, Fast Healthcare Interoperability Resources, Snowflake, Prompt Engineering, Apache Spark, Model Validation, Git, Information Technology, Data Lineage, AWS Glue, Health Level Seven International, Data Management, Machine Learning Operations, Data Delivery, Software Version Control, Data Pipelines - **Published:** May 17, 2026 - **Apply:** https://www.juju.com/job/00000000g0fhvd ## About the Role Bachelor's Degree in Computer Science or Related Field In lieu of degree, equivalent education and/or experience may be considered. Education Preferred Master's Degree in Data Science or Related Field Experience Required: At least 5 years of experience in data engineering. At least 2 years of experience focused on AI/ML data pipelines. Hands on experience working on GenAI projects (chatbot implementations, Natural Language Processing (NLP), Sentiment Analysis, recommendation systems, anomaly detection etc. Preferred: Experience in health plan payer systems and regulatory data handling. Experience with Fast Healthcare Interoperability Resources (FHIR), Health Level Seven (HL7), HIPAA compliance, and healthcare data standards. Skills Required: Proficient skills in Python, SQL, Spark, AWS (Glue, S3, Lambda), Snowflake (Snowpark Container Services), IDMC, prompt engineering, model inference and fine-tuning, RAG and working with MCP, Vector databases. Proficient technical and data engineering skills Solid understanding of supervised and unsupervised machine learning methods, feature engineering, model evaluation, and validation techniques. Ability to operationalize models in production environments, including basic MLOps practices (version control, CI/CD, reproducibility). Ability to communicate complex AI/ML concepts effectively to non-technical stakeholders. Excellent documentation skills, ensuring reproducibility, clarity of assumptions, and transparency of model design. Strong collaboration skills, with proven ability to work cross-functionally with key stakeholders. Analytical problem-solving skills with the ability to translate business challenges into actionable AI/ML solutions. Effective written and verbal communication skills, including documentation of modeling processes, assumptions, and results. Preferred: Experience with FHIR, HL7, HIPAA compliance, and healthcare data standards. Licenses/Certifications Required Licenses/Certifications Preferred AWS Certified Data Engineer Snowflake SnowPro Advanced Certification in GenAI Certification in MLOps Platforms Required Training Required: Data pipeline development and cloud platform training. Preferred: Healthcare compliance and regulatory training. ## Description The Artificial Intelligence Data Engineer II designs, develops, and manages scalable data pipelines and feature stores that enable AI/Machine Learning (ML) model training and deployment across the enterprise. This position collaborates with technical team members to automate data flows, integrate structured and unstructured data sources, and optimize performance for large-scale processing. The AI Data Engineer II also implements data quality validation, metadata management, and lineage tracking to ensure trusted data delivery for AI applications in compliance with healthcare regulations. Duties Design and implement scalable data pipelines for AI/ML workloads. Develop and deploy AI/ML solutions using Python, Snowpark, or cloud-native ML services. Build and manage feature stores to support model training and inference. Integrate structured and unstructured data sources from internal and external systems. Collaborate with data scientists to understand data requirements and optimize pipelines. Implement data quality checks, metadata tagging, and lineage tracking. Ensure compliance with Health Insurance Portability and Accountability Act (HIPAA), Centers for Medicare and Medicaid Services (CMS), and enterprise data governance standards. Automate data ingestion and transformation using tools like AWS Glue, Snowflake, and Informatica Data Management Cloud (IDMC). Implement DevOps/MLOps and Continuous Integration (CI)/Continuous Delivery (CD) pipelines using git actions or similar tools. Monitor pipeline performance and troubleshoot issues in production environments. Contribute to backlog grooming and sprint planning for AI data initiatives. 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