Experience:

• 7+ years of data analysis and AI/ML engineering experience
• Working knowledge of API or stream-based data extraction processes like Salesforce API and Bulk API
• Hands-on experience in web crawling

Qualification:

• Bachelor’s degree in computer science, Statistics, Informatics, Information Systems or another quantitative field

Responsibilities:

• Develop, maintain, and optimize data pipelines and workflows with Feature Store support
• Design, develop, implement, and architect scalable pipelines with performance and scalability in mind
• Identify and redact organization and customer sensitive information using Gen AI and NLP techniques
• Detect anomalies and unusual patterns in data using AI/ML techniques
• Extract and summarize text data using LLM/SLM-based applications and NLP techniques
• Implement and automate end-to-end observability solutions for data pipelines
• Build AI tools and agent-based workflows in Databricks for data transformations and pipeline execution
• Build AI applications that interact with MCP servers to extract required data from different sources and tools
• Implement advanced data transformations and quality checks for accuracy, completeness, security, and consistency
• Integrate data from diverse sources for ingestion, transformation, and storage using AWS S3 and potentially Snowflake
• Create advanced data models and schemas
• Ensure data governance and data management best practices

Additional responsibilities:

• Support semi-structured and unstructured data pipelines, including text, tables, images, video, and audio
• Work with SQL data warehouse administration where applicable
• Use workflow automation for pipeline and delivery processes
• Perform root cause analysis

Nice to have:

• Snowflake administration preference

More skills:

Data pipelines, Data ingestion, Data transformation, Anomaly detection, SLM, Text summarization, Observability, Databricks, MCP servers, AWS S3, Snowflake, Web crawling, Web scraping, AWS Glue, AWS EMR, AWS EC2, AWS RDS, Monitoring, IAM, Kafka, Spark, Kafka Streaming, Github actions, RCA, API data extraction, Stream-based data extraction

Other:

• Onsite role with expectation to primarily work from an HPE office
• Category: Engineering & QA
• Job level: TCP_03
• HPE Networking, Digital Experience & Automation (DEA) team
• Focus on self-service, intelligent automation, omnichannel experiences, and process optimization
• Collaboration with technology providers, engineering, and business teams
• Health & wellbeing benefits
• Personal & professional development programs
• Unconditional inclusion and flexibility to manage work and personal needs
• HPE is an equal employment opportunity, veterans, disabled, and LGBT employer
• Recruitment fraud warning and verification guidance