Role overview
- Develops data structures and pipelines aligned to established standards and guidelines to organize, collect, standardize and transform data that helps generate insights and address reporting needs.
- Focuses on ensuring data quality during ingest, processing as well as final load to the target tables.
- Creates standard ingestion frameworks for structured and unstructured data as well as checking and reporting on the quality of the data being processed.
- Creates standard methods for end users / downstream applications to consume data including but not limited to database views, extracts and Application Programming Interfaces.
- Develops and maintains information systems (e.g., data warehouses, data lakes) including data access Application Programming Interfaces.
- Participates in the implementation of solutions via data architecture, data engineering, or data manipulation on both on-prem platforms like Kubernetes and Teradata as well as Cloud platforms like Databricks.
- Determines the appropriate storage platform across different on-prem (minIO and Teradata) and Cloud (AWS S3, Redshift) depending on the privacy, access and sensitivity requirements.
- Understands the data lineage from source to the final semantic layer along with the transformation rules applied to enable faster troubleshooting and impact analysis during changes.
- Collaborates with technology and platform management partners to optimize data sourcing and processing rules to ensure appropriate data quality as well as process optimization.
- Creates and establishes design standards and assurance processes for software, systems and applications development to ensure compatibility and operability of data connections, flows and storage requirements. Reviews internal and external business and product requirements for data operations and activity and suggests changes and upgrades to systems and storage to accommodate ongoing needs.
- Develops strategies for data acquisition, archive recovery, and database implementation.
- Manages data migrations/conversions and troubleshooting data processing issues.
- Understands the data sensitivity, customer data privacy rules and regulations and applies them consistently in all Information Lifecycle Management activities.
- Identifies and reacts to system notification and log to ensure quality standards for databases and applications. Solves abstract problems beyond single development language or situation by reusing data file and flags already set.
- Solves critical issues and shares knowledge such as trends, aggregate, quantity volume regarding specific data sources.
- Consistent exercise of independent judgment and discretion in matters of significance.
- Regular, consistent and punctual attendance. Must be able to work nights and weekends, variable schedule(s) as necessary.
- Other duties and responsibilities as assigned.
What we're looking for
- Generative AI Application Development: Build AI-driven applications and agentic workflows that interact with structured and unstructured data, external APIs, and enterprise systems.
- Prompt Engineering & Optimization: Design, test, and refine prompts for large language models (LLMs), including instruction tuning, few-shot learning, and chain-of-thought prompting.
- Retrieval-Augmented Generation (RAG): Implement RAG pipelines to enhance model knowledge with external data sources, orchestrate tool usage, and enforce guardrails to mitigate errors or bias.
- LLM & Multimodal Integration: Integrate LLMs and multimodal AI (text, image, audio) into products and workflows, ensuring reliability, accuracy, and ethical compliance.
- Performance Measurement & Evaluation: Develop prompt evaluation frameworks and monitor model outputs with both offline and online metrics to ensure high-quality AI responses.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Ai Engineer Machine Learning Ai