CodeTiburon

CodeTiburon is looking for a Senior Backend Engineer (LLM & AI Infrastructure) to join our team and work closely with one of our long-term clients.

About the project:

We are a deep-tech company building state-of-the-art LLM-based assistants augmented with knowledge graphs. The platform focuses on combining large language models with structured knowledge systems to create more accurate, reliable, and scalable AI assistants.
 

The project involves building robust backend infrastructure that powers AI applications, enabling scalable model inference, retrieval systems, and knowledge graph integrations within a multi-tenant SaaS environment.
 

Responsibilities:
 

• Architect, deploy, and operate scalable backend services and multi-tenant SaaS systems in Kubernetes-based cloud environments
• Design and operate high-throughput data pipelines (batch and streaming)
• Build APIs and services integrating LLM inference, retrieval systems, and knowledge graphs
• Productionize research-grade AI systems into reliable distributed systems
   

Requirements (Must Have):
 

• 7+ years of backend engineering experience
• Strong experience with data pipelines and SaaS architectures
• Strong proficiency in Python
• Deep understanding of distributed systems and cloud-native environments
• Strong hands-on experience with Kubernetes in production environments. Candidates should be comfortable deploying, operating, scaling, and troubleshooting distributed applications on Kubernetes and have practical experience with its core ecosystem (networking, storage, observability, autoscaling, and CI/CD integration).
   

Nice to Have:
 

• Go and/or Scala experience
• Hands-on experience with LLM systems (RAG, model serving, evaluation)
• Experience with knowledge graphs, vector databases, or hybrid retrieval architectures
  
   

Ideal for engineers who enjoy building production-grade systems around cutting-edge AI research.

 

If this sounds like you and your experience aligns with most of the qualifications above, we’d love to hear from you. Please send us your CV.

Thank you for your interest. Each application is carefully reviewed, and candidates whose experience and background best align with the role will be contacted regarding the next steps.

CodeTiburon is looking for an AI Research Engineer — Neuro-Symbolic Reasoning & Generative AI to join our team and work closely with one of our long-term clients.
 

About the Project

Our client is an applied AI lab specializing in formal knowledge representation and next-generation reasoning. The project is dedicated to building “semantic AI” capable of deep, grounded reasoning over complex structures such as texts, arguments, and intellectual traditions.
 

The core mission is to combine the power of modern LLMs with the rigor of neuro-symbolic methods and Knowledge Graphs. By focusing on domains with the highest logical standards, the team aims to set new benchmarks for responsible and intelligent AI behavior across the industry.
 

About the Role:

You will be employed by CodeTiburon and embedded into the client’s research team on a long-term basis. Working alongside a Senior Applied Scientist, you will help transform research ideas into production-quality systems and reproducible experiments.

While the Applied Scientist defines research directions and designs novel algorithms, you will be responsible for implementing experiments, fine-tuning models, building evaluation frameworks, developing data and reasoning pipelines, and iterating toward robust, production-ready solutions.

You will serve as a key engineering partner in the research process—proposing implementation strategies, identifying engineering constraints early, and translating hypotheses into measurable results.

 

Topics You Will Work On:

• Experiment Implementation. Translate research designs from the Applied Scientist into clean, reproducible Python experiments—covering neuro-symbolic architectures (LTN, DeepProbLog, LARK), hybrid LLM+KG pipelines, and RAG systems.
• Knowledge Graph & Ontology Engineering. Build and maintain tooling around RDF/OWL 2 ontologies, SPARQL/Cypher queries, and graph databases (Neo4j, Amazon Neptune) to feed downstream reasoning modules.
• RAG/KG Pipeline Development/Integration. Implement and iterate on retrieval-augmented generation/KG pipelines—combining dense, sparse, and structured retrieval over heterogeneous corpora with provenance tracking.
• LLM Fine-Tuning & Evaluation. Run supervised and RLHF/DPO fine-tuning experiments; build evaluation harnesses that include symbolic verification layers, benchmarks, and regression suites.
• Research Infrastructure. Own the experiment tracking, dataset pipelines, and MLOps scaffolding (MLflow, SageMaker, Vertex AI) that let the team iterate quickly and reproduce results reliably.
• Literature & Prototyping. Stay current with relevant publications; rapidly prototype ideas from newly published methods and assess their fit against the client’s goals.
  
   

Required Qualifications
 

Education:

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, AI Planning, Machine Learning, Cognitive Science, Formal methods, or a closely related field.
 

Language Skills:

Professional working proficiency in English, both written and spoken, with the ability to participate in technical discussions, read and implement research papers, document findings, and collaborate effectively in an international research environment (Strong B2/C1).

Core Technical Skills:

• Generative AI / LLMs: solid working knowledge: transformer architecture, prompt engineering, RAG, tool use, fine-tuning (SFT/LoRA);
• Machine Learning: proficient: supervised/unsupervised learning, representation learning, experiment design;
• Neuro-Symbolic Concepts: familiar: exposure to at least one framework (s.t. LTN, DeepProbLog, NSCL, LARK) or symbolic AI coursework; eager to deepen;
• Knowledge Graphs: working knowledge: graph data models, SPARQL or Cypher queries, at least one graph DB (Neo4j or equivalent);
• Software Engineering: strong production Python; unit testing, version control, CI basics
   

Experience:

• Proven ability to independently implement research ideas, build reproducible experiments, and contribute to production-ready AI systems.
• Demonstrated ability to implement models and algorithms from research papers with minimal hand-holding.
• 3–5 years of hands-on experience in ML/AI engineering, research engineering, or a closely related role (internships and research assistant positions count).
• Experience with experiment tracking tools (MLflow, Weights & Biases, or similar) and reproducible research practices.
• Experience working with cloud-native ML infrastructure on AWS, GCP, or Azure.
   

Preferred Qualifications:

• Exposure to formal knowledge representation: OWL 2, RDF/RDFS, description logics, or first-order logic formalisms.
• Familiarity with agentic AI frameworks (tool-use chains, multi-step planning, multi-agent coordination).
• Experience with LLM evaluation and red-teaming for factual accuracy, logical consistency, or safety.
• Contributions to open-source ML/NLP/KR projects or co-authorship on an arXiv pre-print or workshop paper.
• Exposure to formal verification, SAT/SMT solvers, or answer set programming.
• Experience with low-resource or domain-specific fine-tuning of foundation models.
• Familiarity with graph neural networks or heterogeneous graph transformers.
• Experience with AI coding tools such as Claude Code or Cursor.
• Proven ability to deliver production-ready artifacts.
   

Responsibilities:

• Implement, run, and iterate on experiments designed by the Applied Scientist; maintain clean, well-documented, reproducible code.
• Build and maintain RAG pipelines, knowledge graph integrations, and neuro-symbolic prototype systems.
• Design and execute evaluation harnesses—combining automated metrics, symbolic checks, and human evaluation—to measure model quality and catch regressions.
• Keep experiment infrastructure (data pipelines, tracking, compute provisioning) reliable and easy for the whole team to use.
• Read relevant papers and proactively surface implementation options or potential issues before they become blockers.
• Collaborate daily with the Applied Scientist, ML engineers, and content teams to align research outputs with product requirements.
• Contribute to internal documentation, design reviews, and code reviews; grow your skills through mentorship from senior researchers.
• Uphold scientific rigour, reproducibility, and responsible AI practices in every piece of work you ship.
• Working closely with the production owners.

If this sounds like you and your experience aligns with most of the qualifications above, we’d love to hear from you. Please send us your CV.

Thank you for your interest. Each application is carefully reviewed, and candidates whose experience and background best align with the role will be contacted regarding the next steps.