I'm helping source for a role at a non-profit that runs a national-scale structured data registry (think: a huge linked-data graph, JSON-LD/CTDL schemas, millions of records). They're in the middle of building out an AI layer on top of it — both for AI-assisted data extraction/publishing and for a downstream "intelligence layer" that lets other systems query and reason over the data (MCP-style interfaces, RAG, agents, the works).

This isn't a "plug in an LLM API and ship it" role. You'd actually be:

• Building and scaling an AI-assisted extraction/transformation pipeline (unstructured web data → structured JSON-LD), with real accuracy metrics and instrumentation, not vibes
• Integrating both hosted LLMs and self-hosted smaller models into production data pipelines
• Designing standardized interfaces (MCP or similar) for programmatic, secure access to the data
• Training/evaluating ML models for classification, extraction, and data quality tasks — actual model eval work, not just prompt tweaking
• Building the core distributed systems underneath all of it — APIs, event-driven microservices, cloud-native architecture

Stack is a mix of .NET, Python, and JS on the frontend, running on AWS/Azure/GCP with Docker/Kubernetes. Data side is Neo4j, CosmosDB, SQL Server/PostgreSQL, S3/Blob storage. Auth via Keycloak/Entra ID. On the AI side: Anthropic/OpenAI/Google APIs, prompt engineering + eval frameworks, RAG, multi-step agent workflows.

Looking for someone with 4+ years combined education/experience, real production experience integrating LLMs (not just API calls — actual pipeline/workflow integration), and solid ML fundamentals (classification, regression, clustering, evaluation methodology).

Happy to answer questions about the actual day-to-day work in the comments if anyone's curious before I share more details.

I'm helping source for a role at a non-profit that runs a national-scale structured data registry (think: a huge linked-data graph, JSON-LD/CTDL schemas, millions of records). They're in the middle of building out an AI layer on top of it — both for AI-assisted data extraction/publishing and for a downstream "intelligence layer" that lets other systems query and reason over the data (MCP-style interfaces, RAG, agents, the works).

This isn't a "plug in an LLM API and ship it" role. You'd actually be:

• Building and scaling an AI-assisted extraction/transformation pipeline (unstructured web data → structured JSON-LD), with real accuracy metrics and instrumentation, not vibes
• Integrating both hosted LLMs and self-hosted smaller models into production data pipelines
• Designing standardized interfaces (MCP or similar) for programmatic, secure access to the data
• Training/evaluating ML models for classification, extraction, and data quality tasks — actual model eval work, not just prompt tweaking
• Building the core distributed systems underneath all of it — APIs, event-driven microservices, cloud-native architecture

Stack is a mix of .NET, Python, and JS on the frontend, running on AWS/Azure/GCP with Docker/Kubernetes. Data side is Neo4j, CosmosDB, SQL Server/PostgreSQL, S3/Blob storage. Auth via Keycloak/Entra ID. On the AI side: Anthropic/OpenAI/Google APIs, prompt engineering + eval frameworks, RAG, multi-step agent workflows.

Looking for someone with 4+ years combined education/experience, real production experience integrating LLMs (not just API calls — actual pipeline/workflow integration), and solid ML fundamentals (classification, regression, clustering, evaluation methodology).

Happy to answer questions about the actual day-to-day work in the comments if anyone's curious before I share more details.

I'm helping source for a role at a non-profit that runs a national-scale structured data registry (think: a huge linked-data graph, JSON-LD/CTDL schemas, millions of records). They're in the middle of building out an AI layer on top of it — both for AI-assisted data extraction/publishing and for a downstream "intelligence layer" that lets other systems query and reason over the data (MCP-style interfaces, RAG, agents, the works).

This isn't a "plug in an LLM API and ship it" role. You'd actually be:

• Building and scaling an AI-assisted extraction/transformation pipeline (unstructured web data → structured JSON-LD), with real accuracy metrics and instrumentation, not vibes
• Integrating both hosted LLMs and self-hosted smaller models into production data pipelines
• Designing standardized interfaces (MCP or similar) for programmatic, secure access to the data
• Training/evaluating ML models for classification, extraction, and data quality tasks — actual model eval work, not just prompt tweaking
• Building the core distributed systems underneath all of it — APIs, event-driven microservices, cloud-native architecture

Stack is a mix of .NET, Python, and JS on the frontend, running on AWS/Azure/GCP with Docker/Kubernetes. Data side is Neo4j, CosmosDB, SQL Server/PostgreSQL, S3/Blob storage. Auth via Keycloak/Entra ID. On the AI side: Anthropic/OpenAI/Google APIs, prompt engineering + eval frameworks, RAG, multi-step agent workflows.

Looking for someone with 7+ years combined education/experience, real production experience integrating LLMs (not just API calls — actual pipeline/workflow integration), and solid ML fundamentals (classification, regression, clustering, evaluation methodology).

$1 ~ 2 K per month (open to discuss)

Happy to answer questions about the actual day-to-day work in the comments if anyone's curious before I share more details.