Senior AI Engineer

Senior AI Engineer

At Allstate, great things happen when our people work together to protect families and their belongings from life's uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers' evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.

We are seeking a Senior AI Engineer on the Enterprise Intelligence Factory team to play a foundational role in building the enterprise Semantic Ontology & Dimension Factory Platform. This platform enables AI-ready analytics by combining semantic ontologies, knowledge graphs, agentic AI, and data engineering to automatically generate business-ready star schemas from heterogeneous enterprise data sources. In this role, you will design and implement agent-driven pipelines that leverage RDF/OWL ontologies, SPARQL, and Large Language Models (LLMs) to perform semantic alignment, dimension mining, and AI-assisted data modelling at scale. You will work at the intersection of AI, semantics, and modern data platforms, helping establish engineering patterns and best practices for the team. This is a hands-on, senior individual-contributor role, ideal for engineers who enjoy building core platform capabilities rather than isolated experiments.

Key Responsibilities:

• Design, build, and maintain agentic AI pipelines (using Google ADK or similar frameworks) to automate semantic mapping, dimension mining, and ontology-driven reasoning.
• Create and evolve enterprise ontologies in RDF/OWL, including upper ontologies and domain extensions aligned to CIM (where applicable), to enable reusable enterprise semantics.
• Engineer LLM-powered services for schema understanding, semantic alignment, ontology enrichment, and AI-assisted metadata generation, with a focus on accuracy, traceability, and scale.
• Implement SPARQL querying and reasoning layers over knowledge graphs to drive downstream transformations and ensure consistent interpretation of business concepts.
• Architect and deliver Python-based microservices and batch pipelines that integrate semantic reasoning with modern data-engineering workflows.
• Build and optimize dimension and fact generation pipelines on Microsoft Fabric (Lakehouse, Spark, SQL, orchestration) to produce business-ready star schemas from heterogeneous sources.
• Define and enforce engineering standards, design patterns, and reusable components for semantic and AI-driven data platforms (quality, observability, security, and performance).
• Partner with data architects, domain SMEs, and governance teams to validate semantic definitions, manage change, and ensure platform scalability and adoption.
• Conduct code reviews, mentor engineers, and influence technical decisions across the platform to raise engineering quality and delivery velocity.

Required Skills & Qualifications:

• 6+ years of professional software engineering experience, with strong proficiency in Python and GenAI.
• Hands-on experience building LLM-based systems using commercial or open-source models.
• Solid understanding of semantic technologies: RDF, OWL, ontologies, knowledge graphs, and SPARQL.
• Experience designing or working with agentic AI frameworks (e.g., Google ADK, LangChain agents, or similar).
• Strong background in data engineering concepts (ETL/ELT, star schemas, metadata-driven pipelines).
• Experience building and operating systems on cloud platforms, preferably Microsoft Azure/Microsoft Fabric.
• Strong problem-solving skills and ability to work in ambiguous, greenfield platform initiatives.

Preferred Skills:

• Experience with enterprise data models (e.g., CIM or canonical models).
• Familiarity with semantic alignment, ontology mapping, or data cataloguing tools.
• Exposure to MLOps/LLMOps, model evaluation, and AI observability.
• Knowledge of distributed systems, CI/CD pipelines, and containerisation.
• Experience building AI-assisted analytics or semantic layers for BI or NLQ use cases.

The hiring manager has flexibility to hire across several levels of seniority. The level will be determined by the selected applicant's skills and competencies.