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

About the RoleWe 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 DoDesign and build data-driven tools that operate on large datasets stored in S3 and SnowflakeImplement pipelines that:Extract specific columns or datasets from SnowflakeGenerate vector embeddings via APIs such as OpenAIStore and manage embeddings in vector databases like PineconeEnable semantic search and similarity-based retrievalDevelop enrichment workflows that:Query structured dataUse LLM APIs to generate new derived columnsWrite enriched results back into SnowflakeBuild reusable internal services and SDKs around embedding generation, prompt orchestration, and data augmentationOptimize performance and cost across AWS infrastructureWork closely with product and data teams to turn use cases into scalable engineering solutionsEnsure reliability, observability, and maintainability of AI-powered pipelinesExample ProjectsTool to extract a single Snowflake column, generate embeddings, push to Pinecone, and expose a semantic search APIBatch enrichment pipeline that queries records from Snowflake, calls OpenAI APIs for structured enrichment, and writes new columns backInternal framework for LLM-based data transformation and validationQuery abstraction layer to make AI-enhanced analytics accessible to non-engineering teamsRequired Qualifications5+ years of software engineering experienceStrong 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 systemsHands-on experience working with LLM APIs (e.g., OpenAI) and embedding workflowsExperience with vector databases (Pinecone or similar)Strong understanding of data modeling, ETL/ELT patterns, and performance optimizationProduction experience in at least one startup environmentAbility to operate independently and ship high-impact systems end-to-endNice to HaveExperience building internal developer platforms or data toolingFamiliarity with prompt engineering and evaluation pipelinesExperience with orchestration frameworks (Airflow, Prefect, Dagster)Exposure to retrieval-augmented generation (RAG) systemsInfrastructure-as-code experience (Terraform, CDK)Experience managing large-scale embedding refresh and re-indexing workflowsWhat Success Looks LikeEngineers and analysts can easily leverage AI-powered data enrichmentEmbedding-based search works reliably at scaleNew AI use cases can be implemented quickly using shared internal toolingSystems are robust, observable, and cost-efficientWhy Join Us?Work on practical, production-grade AI systemsDirect impact on how data is leveraged across the companyStartup speed with real ownership and autonomyOpportunity to define the internal AI platform from the ground upOriginally posted on Himalayas