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.
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Skills
Agentic AI, Agentic Design, AI Frameworks, Large Language Models (LLMs), LLM Guardrails, LLM Orchestration, Ontology, Python (Programming Language)
Compensation
Compensation offered for this role is 100,000.00 - 170,500.00 annually and is based on experience and qualifications.
The candidate(s) offered this position will be required to submit to a background investigation.
Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.
Allstate generally does not sponsor individuals for employment-based visas for this position.
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