What are the responsibilities and job description for the Senior Data and AI Engineer (Insurance Domain) position at Accord Technologies Inc?
Senior Data and AI Engineer (Insurance Domain)
Location: Philadelphia, PA
Position type: Onsite role (need NJ, PA based candidates who can join immediately)
Tax type: W2 contract
Candidate should be available to start by next week.
Job Description
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 (Prot g , 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
Data Engineering & Microsoft Fabric (Good to-Have)
Strong Python engineering skills with experience building data pipelines, ETL/ELT processes, and metadata ingestion frameworks
Experience with Microsoft Fabric ecosystem: OneLake, Lakehouse, Notebooks, Data Factory / pipelines, and Medallion architecture (Bronze/Silver/Gold)
Familiarity with JDBC/ODBC connectors, REST API integration, and file parsing (Excel, CSV, JSON) for metadata extraction
Experience with Trino, Databricks SQL, or equivalent federated query engines
Location: Philadelphia, PA
Position type: Onsite role (need NJ, PA based candidates who can join immediately)
Tax type: W2 contract
Candidate should be available to start by next week.
Job Description
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 (Prot g , 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
Data Engineering & Microsoft Fabric (Good to-Have)
Strong Python engineering skills with experience building data pipelines, ETL/ELT processes, and metadata ingestion frameworks
Experience with Microsoft Fabric ecosystem: OneLake, Lakehouse, Notebooks, Data Factory / pipelines, and Medallion architecture (Bronze/Silver/Gold)
Familiarity with JDBC/ODBC connectors, REST API integration, and file parsing (Excel, CSV, JSON) for metadata extraction
Experience with Trino, Databricks SQL, or equivalent federated query engines
Salary : $50 - $52