Seeking a strong Data Engineer / AI Engineer with expertise in building and operationalizing large-scale AI and NLP solutions on cloud platforms. The ideal candidate should have hands-on experience integrating AI/LLM models into production workflows, developing scalable data pipelines, and processing large volumes of multilingual unstructured text.
Key strengths should include:
- Proficiency in Python and SQL with experience deploying AI/NLP solutions such as document classification, entity extraction, NER, PII masking, de-identification, hybrid search, and LLM integrations.
- Strong knowledge of Apache Airflow for orchestrating end-to-end data pipelines and automating batch processing workflows.
- Experience working with AWS services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.
- Capability to design and maintain large-scale document processing systems handling complex JSON structures, embedded documents, and multilingual content.
- Familiarity with vector search and retrieval systems, including embeddings, pgvector, PostgreSQL/Aurora, GIN indexes, and full-text search.
- Experience with ML lifecycle management using MLflow, Databricks/Azure Databricks, model deployment, monitoring, and evaluation frameworks.
- Strong DevOps practices including GitHub-based development, CI/CD pipelines, schema management, and production support.
What You Will Do
AI Module Integration & Inference Pipelines
- Integrate and adjust inference pipelines for NLP modules including document classification, entity extraction, de-identification (DEID), and LLM-based early trend detection
- Connect DS-coded AI modules into end-to-end production workflows via Airflow DAGs on AWS EKS
- Build and tune hybrid search pipelines combining GTE multilingual dense embeddings with GIN lexical search on Aurora PostgreSQL
- Integrate with OpenAI-based API platform for multilingual query expansion and LLM-driven trend detection
Document Processing & Parsing
- Design and maintain document preprocessing pipelines that parse deeply nested JSON structures (emails with attachments, embedded PDFs) from S3/DataLake
- Handle multilingual unstructured text (English, Spanish, Portuguese, German, Dutch, French, Italian) across 300 GB of claim notes and documents
- Build chunking strategies and metadata extraction for downstream embedding and retrieval workflows
Data Pipeline Engineering
- Author and maintain Airflow DAGs for batch processing (monthly entity refresh, trend detection, DEID pipeline)
- Manage data flow across AWS services: S3, Athena, Glue, Fargate, SQS, Step Functions
- Scale pipelines to handle 500K+ claims and hundreds of millions of text chunks
Production Deployment & Quality
- Deploy and version models using MLflow and Databricks
- Manage schema evolution and migrations using Liquibase on Aurora PostgreSQL
- Instrument pipelines with logging, monitoring, and evaluation scoring for retrieval quality
Area
Skills
Languages
Python (primary), SQL
AI / NLP
LLM API integration, multilingual embeddings (e.g., GTE), hybrid search, text classification, entity extraction, NER, PII masking
Data Pipelines
Apache Airflow, batch orchestration, large-scale unstructured data processing
Cloud & Infrastructure
AWS (S3, Athena, Glue, Fargate, EKS, SQS, Step Functions)
Databases
PostgreSQL / Aurora, pgvector, GIN indexes, full-text search
ML Platform
MLflow, Databricks / Azure Databricks
DevOps
GitHub, CI/CD pipelines
Education
- Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
- Master's degree in Data Science, AI/ML, Computer Science, or Analytics is preferred but not mandatory.
- Relevant cloud or data engineering certifications are advantageous.