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Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.

Lead client relationships and oversee analytics and AI engagements, ensuring on-time delivery, client satisfaction, revenue growth, and cross-functional team performance. Serve as primary client contact, interface with senior leadership, drive business development, and provide thought leadership and solutions that generate business impact.

Design and deliver large-scale data applications and pipelines on-premises and in cloud (AWS/GCP). Implement ETL processes, migrate pipelines to cloud, integrate data science workflows, develop testing and backfill strategies, document and support warehouse users, and participate in agile development.

Lead design, development, and maintenance of scalable ETL pipelines and analytical datasets for large U.S. healthcare data (Medicaid, Medicare, Commercial). Build data workflows and data models using Python and SQL, ensure data quality and HIPAA-compliant PHI handling, optimize large-scale data processing, collaborate with cross-functional teams, document processes, and participate in Agile development.

Deploy and troubleshoot EXLdata.ai in client cloud environments, build and optimize data pipelines (PySpark, Databricks/Snowflake), integrate GenAI agents, drive client value realization, and provide white‑glove managed‑service support and product feedback.

No responsibilities or details were provided. The posting only lists the job title "AI Engineer" with no additional information about duties or expectations.

Senior AWS Analytics Engineer. Posting contains no detailed responsibilities or qualifications beyond the job title.

Design, build, and maintain both front-end and back-end components of web applications as a Full Stack Developer. Collaborate with cross-functional teams to implement features, fix bugs, and support deployment and maintenance.

No responsibilities provided in the job description.

Design and deploy enterprise-grade generative AI systems across the 7-layer stack, select and fine-tune models, build LLMOps pipelines and observability, integrate with cloud platforms and APIs, enforce data protection, manage hallucinations, lead technical strategy, and act as onshore client technical liaison.