- Bedok East Region (Singapore) Singapore
RESHUFFLE.AI PTE. LTD. Hiring! Full Time AI Engineer – Neuro-Symbolic AI in East Region (Singapore), Earn up to SGD 7,000 - Ricebowl
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Job Description
Responsibilities
Job Summary
We 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 Responsibilities
Design, 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 Requirements
Candidates 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 systems
Rule-based systems and inference engines
Logic programming
Knowledge representation and reasoning
Automated reasoning
Constraint solving
Knowledge graphs
Ontologies and semantic modelling
Formal reasoning or formal methods
Decision logic and rule chaining
Multi-step inference systems
Explainable reasoning systems
Hybrid architectures combining neural and symbolic AI
Candidates 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 Requirements
Degree 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 Experience
Hands-on experience with one or more of the following technologies, or equivalent symbolic reasoning technologies, is highly relevant:
Answer Set Programming (ASP) / Clingo
Prolog
Datalog
Z3 / SMT solvers
Constraint programming frameworks
Rule engines such as Drools
RDF / RDFS / OWL
SPARQL
Neo4j or other graph-based knowledge systems
Semantic reasoning engines
Knowledge representation frameworks
Automated theorem-proving or formal reasoning tools
Equivalent technologies and approaches will also be considered.
Neuro-Symbolic AI Experience
The candidate should understand how symbolic reasoning can complement neural AI systems.
Relevant experience may include:
Connecting machine learning or LLM systems to symbolic reasoning engines
Converting natural-language inputs into structured facts, rules or constraints
Using neural systems for interpretation while using symbolic systems for reasoning and decision-making
Validating AI-generated outputs against deterministic rules and constraints
Generating auditable reasoning paths and explanations
Combining probabilistic predictions with deterministic decision logic
Designing systems where conclusions can be traced back to underlying facts and rules
Preferred Experience
Experience 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 Competencies
Strong 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 On
The successful candidate will contribute to next-generation AI systems where reasoning quality, explainability and determinism are critical.
Potential areas include:
Neuro-Symbolic AI
Symbolic AI
Automated reasoning systems
Rule and policy engines
Knowledge-based systems
Decision-support systems
Constraint-based reasoning
Knowledge graphs and ontologies
Explainable AI
Legal and regulatory reasoning
Compliance automation
Complex enterprise decision automation
Multi-criteria decision systems
AI governance and assurance
The objective is to build AI systems that combine the adaptability of modern AI with the
precision, transparency and logical consistency of symbolic reasoning systems
.
We 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 Responsibilities
Design, 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 Requirements
Candidates 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 systems
Rule-based systems and inference engines
Logic programming
Knowledge representation and reasoning
Automated reasoning
Constraint solving
Knowledge graphs
Ontologies and semantic modelling
Formal reasoning or formal methods
Decision logic and rule chaining
Multi-step inference systems
Explainable reasoning systems
Hybrid architectures combining neural and symbolic AI
Candidates 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 Requirements
Degree 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 Experience
Hands-on experience with one or more of the following technologies, or equivalent symbolic reasoning technologies, is highly relevant:
Answer Set Programming (ASP) / Clingo
Prolog
Datalog
Z3 / SMT solvers
Constraint programming frameworks
Rule engines such as Drools
RDF / RDFS / OWL
SPARQL
Neo4j or other graph-based knowledge systems
Semantic reasoning engines
Knowledge representation frameworks
Automated theorem-proving or formal reasoning tools
Equivalent technologies and approaches will also be considered.
Neuro-Symbolic AI Experience
The candidate should understand how symbolic reasoning can complement neural AI systems.
Relevant experience may include:
Connecting machine learning or LLM systems to symbolic reasoning engines
Converting natural-language inputs into structured facts, rules or constraints
Using neural systems for interpretation while using symbolic systems for reasoning and decision-making
Validating AI-generated outputs against deterministic rules and constraints
Generating auditable reasoning paths and explanations
Combining probabilistic predictions with deterministic decision logic
Designing systems where conclusions can be traced back to underlying facts and rules
Preferred Experience
Experience 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 Competencies
Strong 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 On
The successful candidate will contribute to next-generation AI systems where reasoning quality, explainability and determinism are critical.
Potential areas include:
Neuro-Symbolic AI
Symbolic AI
Automated reasoning systems
Rule and policy engines
Knowledge-based systems
Decision-support systems
Constraint-based reasoning
Knowledge graphs and ontologies
Explainable AI
Legal and regulatory reasoning
Compliance automation
Complex enterprise decision automation
Multi-criteria decision systems
AI governance and assurance
The objective is to build AI systems that combine the adaptability of modern AI with the
precision, transparency and logical consistency of symbolic reasoning systems
.
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