AI Engineer – Neuro-Symbolic AI

Job SummaryWe are seeking an experienced AI Engineer to design, develop and deploy advanced artificial intelligence solutions with a strong focus on Neuro-Symbolic AI and Symbolic AI systems.The successful candidate must have practical experience in symbolic reasoning technologies such as rule engines, logic programming, knowledge representation, ontologies, constraint solving, inference systems, knowledge graphs or formal reasoning, together with experience integrating these approaches with modern AI and machine learning systems.This role is intended for an engineer who can build AI systems that go beyond probabilistic model outputs by incorporating explicit rules, structured knowledge, deterministic reasoning, explainability and traceable decision paths.Hands-on experience with symbolic AI or Neuro-Symbolic AI systems is mandatory.Key ResponsibilitiesDesign, develop and deploy Neuro-Symbolic AI architectures combining neural AI techniques with symbolic reasoning systems.Develop and maintain symbolic reasoning engines, rule-based systems and structured decision logic.Translate business rules, policies, domain knowledge and complex decision criteria into machine-executable symbolic representations.Design systems using logic programming, rules, constraints, ontologies, knowledge graphs and formal reasoning techniques.Integrate Large Language Models or other machine learning models with deterministic symbolic systems where appropriate.Develop inference pipelines capable of generating traceable and explainable reasoning paths.Implement systems for rule chaining, multi-stage inference and dependency-based reasoning.Design representations for facts, rules, relationships, constraints and domain knowledge.Develop mechanisms for reasoning validation, conflict detection, consistency checking and explanation generation.Build APIs, backend services and reusable reasoning components for integration with enterprise systems.Evaluate reasoning systems for correctness, consistency, completeness, explainability and computational performance.Develop prototypes and production solutions involving automated reasoning and intelligent decision support.Work closely with domain experts, software engineers and business stakeholders to translate complex domain requirements into executable AI logic.Implement appropriate testing, monitoring, governance and documentation for AI and reasoning systems.Research and evaluate emerging developments in Neuro-Symbolic AI, symbolic reasoning, knowledge representation and automated decision systems.Mandatory RequirementsCandidates must possess demonstrable hands-on experience with Symbolic AI, Neuro-Symbolic AI or automated reasoning systems.Relevant experience should include one or more of the following areas:Symbolic reasoning systemsRule-based systems and inference enginesLogic programmingKnowledge representation and reasoningAutomated reasoningConstraint solvingKnowledge graphsOntologies and semantic modellingFormal reasoning or formal methodsDecision logic and rule chainingMulti-step inference systemsExplainable reasoning systemsHybrid architectures combining neural and symbolic AICandidates whose experience is limited primarily to conventional machine learning, prompt engineering or generative AI without symbolic-system experience will not meet the core requirements of this role.Technical RequirementsDegree in Computer Science, Artificial Intelligence, Software Engineering, Mathematics or a related technical discipline.Strong software development skills, preferably using Python.Strong understanding of algorithms, data structures and software engineering principles.Practical experience implementing symbolic reasoning or decision systems.Understanding of declarative programming and rule-based computation.Experience modelling complex domain rules and relationships.Experience developing backend services and APIs.Familiarity with databases, including relational and/or graph databases.Experience with Git and modern software development practices.Understanding of software testing, version control and production deployment practices.Symbolic AI Technology ExperienceHands-on experience with one or more of the following technologies, or equivalent symbolic reasoning technologies, is highly relevant:Answer Set Programming (ASP) / ClingoPrologDatalogZ3 / SMT solversConstraint programming frameworksRule engines such as DroolsRDF / RDFS / OWLSPARQLNeo4j or other graph-based knowledge systemsSemantic reasoning enginesKnowledge representation frameworksAutomated theorem-proving or formal reasoning toolsEquivalent technologies and approaches will also be considered.Neuro-Symbolic AI ExperienceThe candidate should understand how symbolic reasoning can complement neural AI systems.Relevant experience may include:Connecting machine learning or LLM systems to symbolic reasoning enginesConverting natural-language inputs into structured facts, rules or constraintsUsing neural systems for interpretation while using symbolic systems for reasoning and decision-makingValidating AI-generated outputs against deterministic rules and constraintsGenerating auditable reasoning paths and explanationsCombining probabilistic predictions with deterministic decision logicDesigning systems where conclusions can be traced back to underlying facts and rulesPreferred ExperienceExperience developing enterprise-grade AI or automated decision-support systems.Experience with complex rule engines or policy automation.Experience with multi-step reasoning and rule-chain execution.Experience designing knowledge representations for specialised domains.Experience with AI systems operating in regulated or high-assurance environments.Understanding of explainable AI and AI governance.Familiarity with AI governance frameworks such as ISO/IEC 42001, NIST AI RMF or equivalent.Experience building systems where decisions must be auditable, reproducible and explainable.Experience with cloud environments such as AWS, Azure, Google Cloud or Oracle Cloud Infrastructure.Familiarity with containerisation and CI/CD practices.Key CompetenciesStrong analytical and logical reasoning ability.Strong understanding of symbolic computation and knowledge representation.Ability to convert complex policies and domain rules into formal logic.Ability to identify relationships, dependencies and rule chains across large rule sets.Understanding of forward chaining, backward chaining and other inference approaches.Ability to reason about conflicting, incomplete or uncertain information.Strong software engineering discipline.Strong problem-solving capability.Ability to work closely with domain specialists and translate domain knowledge into executable logic.Strong written and verbal communication skills.What You Will Work OnThe successful candidate will contribute to next-generation AI systems where reasoning quality, explainability and determinism are critical.Potential areas include:Neuro-Symbolic AISymbolic AIAutomated reasoning systemsRule and policy enginesKnowledge-based systemsDecision-support systemsConstraint-based reasoningKnowledge graphs and ontologiesExplainable AILegal and regulatory reasoningCompliance automationComplex enterprise decision automationMulti-criteria decision systemsAI governance and assuranceThe objective is to build AI systems that combine the adaptability of modern AI with the precision, transparency and logical consistency of symbolic reasoning systems.