V

Artificial Intelligence Engineer

Voto Consulting LLC
3 hours ago
Full-time
On-site
Philadelphia, Pennsylvania, United States
Role: Sr. AI Engineer & Data Engineer Experience : 10 Yrs (W2 Only) Interview : Virtual Location: Philadelphia, PA – Hybrid work from day one

Have you got what it takes to succeed The following information should be read carefully by all candidates.

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: xsgimln 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