Job Description:Role OverviewWe are seeking an early-career Software Engineer with a strong foundation in applied AI and machine learning, supported by hands-on experience with dbt Core, SQL, and Databricks on AWS. This role is intended for candidates who already understand the fundamentals of building, deploying, and supporting AI systems and want to apply those skills in a production data platform. Suitable interns with relevant hands-on experience are encouraged to apply.You will work on AI and ML workflows end-to-end, from preparing high-quality training data to supporting model execution and evaluation, with hands-on ownership and guidance as needed.Core ResponsibilitiesAI & Machine Learning (Primary Focus)- Contribute to the development and support of machine learning and AI pipelines in a production environment.
- Assist in building and maintaining training and inference workflows on Databricks.
- Prepare and validate feature datasets used by ML models, ensuring correctness, consistency, and timeliness.
- Support LLM-enabled use cases, such as:
- embedding generation
- semantic search
- retrieval-augmented generation (RAG)
- Track and evaluate model experiments using tools such as MLflow.
- Monitor model inputs and outputs for data quality issues and unexpected behavior.
- Understand how upstream data changes affect model performance, stability, and bias.
dbt Core & AI-Ready Data Modeling- Develop and maintain dbt Core models that serve as trusted inputs to AI and ML pipelines.
- Build incremental models and snapshots to support historical and feature-based modeling.
- Apply dbt tests and documentation to ensure data quality for AI consumption.
- Work with contracted, curated datasets while understanding upstream raw source tables and lineage.
- Participate in code reviews and follow established dbt and SQL standards.
SQL & Data Foundations- Write and optimize SQL transformations that support feature engineering and model training.
- Debug data anomalies and model input issues using SQL and dbt test results.
- Understand how joins, aggregations, and window functions impact performance and correctness.
Databricks & Lakehouse Engineering- Use Databricks to execute and monitor data and ML workloads.
- Work with Delta Lake tables across bronze, silver, and gold layers.
- Support batch and micro-batch processing using Spark.
- Understand data governance concepts, including access control and lineage, within Unity Catalog.
AWS & Cloud Platform- Work with AWS-based lakehouse architectures, primarily using Amazon S3.
- Understand IAM roles, permissions, and secure access patterns.
- Assist with troubleshooting and optimizing cloud-based AI and data workflows.
Requirements/Qualifications:Required Qualifications- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience).
- Hands-on experience with AI and machine learning, including model training or applied AI projects.
- Working knowledge of dbt Core, including models, tests, and documentation.
- Strong SQL skills with experience transforming analytical datasets.
- Hands-on experience with Databricks on AWS (academic, internship, or project-based).
- Familiarity with Python for data processing or ML workflows.
- Experience using Git or another version control system.
Travel Time:0% - 25%
Physical Attributes:Hearing, Seeing, Talking, Works Alone, Works Around Others
Physical Requirements:80% sitting, 10% standing, 10% walking, 100% inside
