AI MLOPS/LLMOps Engineer

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.

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

- 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.

- 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

- 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

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

- 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.

Databases PostgreSQL / Aurora, pgvector, GIN indexes, full-text search ML Platform MLflow, Databricks / Azure Databricks DevOps GitHub, CI/CD pipelines

Production Deployment & Quality - Deploy and version models using MLflow and Databricks - Manage schema evolution and migrations using Liquibase on Aurora PostgreSQL

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

- 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.

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

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)

- 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.

- 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

- 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

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