Role: Sr. AI Engineer & Data Engineer

Experience : 10 Yrs (W2 Only)

Interview : Virtual

Location: Philadelphia, PA – Hybrid work from day one

The role owns the full technical stack from the architecture slide: connectors and ingestion framework, OneLake Medallion staging, GraphDB triple store, Vector Index, Agentic RAG orchestrator, LLM gateway, guardrails, and the consumption UI with conversational chat, SPARQL trace explainability, and graph explorer.

Knowledge Graph & Semantic Technologies (Must-Have)

3+ years hands-on experience with graph databases (GraphDB, Neo4j, Stardog)in a production or advanced PoC context
Working proficiency with semantic web standards
Experience loading, validating, and querying ontologies in a triple store environment
Familiarity with ontology authoring tools (Protégé, Metaphactory) sufficient to collaborate with the Data Consultant on model iterations

AI / ML Engineering & LLM Integration (Must-Have)

Demonstrated experience building RAG (Retrieval-Augmented Generation) pipelines, ideally with agentic orchestration patterns
Hands-on experience with vector databases (Azure AI Search, pgvector, Pinecone, Weaviate, or Qdrant) for embedding and retrieval
Experience integrating LLM APIs (Anthropic Claude, OpenAI GPT, or Azure OpenAI) with prompt engineering, guardrails, and citation enforcement
Familiarity with NL-to-SPARQL or NL-to-SQL generation techniques, including few-shot prompting and schema-grounding approaches
Understanding of AI safety guardrails: prompt injection defense, output sandboxing, and confidence scoring

Delivery & Collaboration (Must-Have)

Comfortable operating in an accelerated 8-week delivery timeline with weekly milestone gates and hard dependencies
Ability to work closely with a Data Modeller/Ontologist to translate conceptual models into working technical implementations
Experience in financial services or insurance data environments is preferred but not required, provided strong technical depth in the above areas