Engineer, AI Engineer (Applied​/Software)

Job Detail Experience Level Mid Level Degree Type Bachelor of Science (BS)
Employment Full Time Working Type Hybrid Job Reference Salary Type Annually Industry Private Equity;

Venture Capital and Private Equity Selling Points Drive innovation in AI and data engineering with cutting-edge technologies. Collaborate in a hybrid environment fostering growth and development. Deliver impactful solutions for enterprise-scale AI applications.

Job Description Engineer Overview The Engineer role focuses on transforming data and AI architecture into production systems.

Collaborate within a federated data model to implement shared platforms and reusable patterns for AI initiatives.

Contribute to the development of retrieval-augmented generation (RAG) patterns and AI-ready data products.

Operate pipelines, semantic layers, and retrieval systems to support analytics and generative AI applications.

Partner with domain teams to deliver trusted, scalable AI-consumable data solutions.

Focus on delivering foundational AI-ready data pipelines and reusable engineering patterns.

Work in a hybrid environment with a commitment to innovation and collaboration.

Engineer

Key Responsibilities & Duties Develop AI-ready data pipelines, embedding generation, indexing, and refresh workflows.

Implement retrieval-augmented generation components and vector store integrations.

Maintain interfaces for agents and copilots to query enterprise data safely.

Design and maintain ELT, streaming, and transformation pipelines using modern tools.

Build semantic models and analytical/dimensional models for AI grounding.

Implement data quality checks, lineage capture, and pipeline observability.

Collaborate with governance teams to ensure rigorous data consumption standards.

Mentor junior engineers and contribute to architectural design reviews.

Engineer

Job Requirements Bachelor's degree in computer science, data engineering, or related field required.
6+ years of technical experience, including 1–2 years in AI/ML systems production.

Proficiency in Python, SQL, and distributed processing frameworks like Spark.

Experience with modern data stacks such as dbt, Fivetran, and Snowflake.

Hands-on expertise with AI platforms and tools like AWS Bedrock and OpenAI APIs.Knowledge of retrieval-augmented generation pipelines and vector databases.

Strong engineering craft with a focus on clean code and automated testing.

Ability to operate in federated data models and regulated enterprise environments.