Senior Data & AI Platform Engineer (AWS, Snowflake, Vector Search)

About the Role

We are looking for a Senior Data & AI Platform Engineer to build internal tools and services on top of our large-scale data infrastructure. Your primary focus will be developing systems that leverage vector embeddings, LLM APIs, and semantic search to unlock value from structured and unstructured data.

This is a hands-on engineering role for someone who enjoys building practical AI-powered tools — not just experiments — and shipping them into production in a fast-moving startup environment.

What You’ll Do

• Design and build data-driven tools that operate on large datasets stored in S3 and Snowflake

• Implement pipelines that:

  • Extract specific columns or datasets from Snowflake

  • Generate vector embeddings via APIs such as OpenAI

  • Store and manage embeddings in vector databases like Pinecone

  • Enable semantic search and similarity-based retrieval

• Develop enrichment workflows that:

  • Query structured data

  • Use LLM APIs to generate new derived columns

  • Write enriched results back into Snowflake

• Build reusable internal services and SDKs around embedding generation, prompt orchestration, and data augmentation

• Optimize performance and cost across AWS infrastructure

• Work closely with product and data teams to turn use cases into scalable engineering solutions

• Ensure reliability, observability, and maintainability of AI-powered pipelines

Example Projects

• Tool to extract a single Snowflake column, generate embeddings, push to Pinecone, and expose a semantic search API

• Batch enrichment pipeline that queries records from Snowflake, calls OpenAI APIs for structured enrichment, and writes new columns back

• Internal framework for LLM-based data transformation and validation

• Query abstraction layer to make AI-enhanced analytics accessible to non-engineering teams

Required Qualifications

• 5+ years of software engineering experience

• Strong backend engineering skills (Python preferred; other modern languages acceptable)

• Solid experience with:

  • AWS (IAM, Lambda, ECS/EKS, S3, networking, security best practices)

  • Data warehousing (Snowflake preferred)

  • API design and distributed systems

• Hands-on experience working with LLM APIs (e.g., OpenAI) and embedding workflows

• Experience with vector databases (Pinecone or similar)

• Strong understanding of data modeling, ETL/ELT patterns, and performance optimization

• Production experience in at least one startup environment

• Ability to operate independently and ship high-impact systems end-to-end

Nice to Have

• Experience building internal developer platforms or data tooling

• Familiarity with prompt engineering and evaluation pipelines

• Experience with orchestration frameworks (Airflow, Prefect, Dagster)

• Exposure to retrieval-augmented generation (RAG) systems

• Infrastructure-as-code experience (Terraform, CDK)

• Experience managing large-scale embedding refresh and re-indexing workflows

What Success Looks Like

• Engineers and analysts can easily leverage AI-powered data enrichment

• Embedding-based search works reliably at scale

• New AI use cases can be implemented quickly using shared internal tooling

• Systems are robust, observable, and cost-efficient

Why Join Us?

• Work on practical, production-grade AI systems

• Direct impact on how data is leveraged across the company

• Startup speed with real ownership and autonomy

• Opportunity to define the internal AI platform from the ground up