AI MLOPS/LLMOps Engineer
About the Role
We are looking for an AI Engineer with strong data pipeline experience to help operationalize and connect multiple DS-coded AI modules into production-grade systems. This is a technically deep role, heavier on AI engineering and data pipeline work — UI expertise is handled by a separate team.
Key Skills for This Role
Full Job Posting
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
- 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
Qualifications
- 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.
- 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.
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