Senior Software Engineer – Neuro-Symbolic AI & LLMs

Indian AI Research Organisation (IAIRO); Hinoba-an; On-site; PHP 1,000,000 - 2,000,000; Full time

Job summary

Indian AI Research Organisation (IAIRO) seeks a Senior Software Engineer to lead the architecture and implementation of hybrid neuro-symbolic AI systems. You will build and optimize knowledge bases, train LLMs, and integrate neural and symbolic components for enterprise-scale applications.

The role requires deep Python expertise, experience with LLMs, and a passion for turning cutting-edge research into production-ready code with robust, scalable backends and clear explainability.

Qualifications

• MS or PhD in Computer Science or AI or equivalent industry experience.
• 5+ years of software engineering experience with strong Python proficiency.
• Extensive LLM training experience including pretraining, fine-tuning, and RAG architectures.
• Strong knowledge of Knowledge Graphs (SPARQL/Cypher, RDF/OWL) and symbolic reasoning.
• Experience with distributed training libraries such as DeepSpeed or Megatron-LM.

Responsibilities

• Neuro-Symbolic integration: design architectures linking LLMs with Knowledge Graphs for grounding and reasoning.
• LLM training & optimization: lead training and fine-tuning on domain datasets with modern techniques.
• Knowledge Graph engineering: build scalable knowledge representations, ontologies, and stores.
• System architecture: develop high-performance backends for Neuro-Symbolic systems with low latency inference.
• Research-to-production: translate cutting-edge research into robust production code.
• Mentorship: lead engineers and researchers toward rigorous engineering and AI development.

Skills

Python

Knowledge Graphs

LLM Training

Distributed Training

PyTorch

TensorFlow

CUDA

RAG

Education

MS or PhD in CS/AI

Tools

DeepSpeed

Megatron-LM

CUDA

GPU Orchestration

Job description

Senior Software Engineer – Neuro-Symbolic AI & LLMs

As a Senior Software Engineer for a platform that creates small/compact, custom/target and neurosymbolic AI models for Enterprises, you will lead the architectural development and implementation of hybrid AI systems. You will take a hands‑on role in building and leveraging knowledge bases, going beyond off-the-shelf LLMs to train, fine‑tune, and optimize large‑scale models that work in tandem with structured knowledge systems.

Key Responsibilities

• Neuro-Symbolic Integration: Design and implement architectures that link LLMs with Knowledge Graphs (KGs) to ensure factual grounding and commonsense reasoning.


• LLM Training & Optimization: Lead the training and fine‑tuning of Large Language Models (LLMs) on domain‑specific datasets (e.g., healthcare, pharmaceuticals) using techniques such as LoRA, QLoRA, and distributed training.


• Knowledge Graph Engineering: Build and maintain scalable knowledge representation systems, ontologies, and triple stores that serve as the truth layer for AI agents.


• System Architecture: Develop high‑performance backends for Neuro‑Symbolic AI systems, ensuring low‑latency inference and seamless integration between neural and symbolic components.


• Research‑to‑Production: Translate cutting‑edge research papers on C3AN methodologies into robust, production‑ready code.


• Mentorship: Lead a team of engineers and researchers, fostering a culture of rigorous engineering and innovative AI development.

Required Skills & Experience

• Education: M.S. or PhD in Computer Science, AI, or a related field (or equivalent industry experience).


• Core Engineering: 5+ years of software engineering experience with deep proficiency in Python.


• LLM Expertise: Extensive experience in Large Language Model training, including pretraining, instruction tuning, and RAG (Retrieval‑Augmented Generation) architectures.


• Symbolic AI: Strong foundation in Knowledge Graphs, SPARQL/Cypher, RDF/OWL, and logic‑based reasoning frameworks.


• ML Frameworks: Expertise in PyTorch or TensorFlow, along with experience using distributed training libraries such as DeepSpeed or Megatron‑LM.


• Cloud & Infrastructure: Familiarity with GPU orchestration (CUDA, Triton) and deploying models on high‑performance compute environments (e.g., IndiaAI Cloud).

Bonus Qualifications

• Experience with Neuro‑Symbolic frameworks, Knowledge Graphs, and logical reasoning systems (e.g., Logic Tensor Networks, DeepProbLog).


• Contributions to open‑source AI projects or publications in leading venues such as NeurIPS, ICML, or AAAI.


• Experience in domains such as manufacturing, pharmaceuticals, or healthcare.

C3AN is a flagship project focused on the next generation of AI: Custom (solving specific tasks or domain‑specific problems), compact (small or right‑sized), Neuro‑Symbolic AI. The project aims to address the limitations of purely data‑driven models such as hallucinations and limited reasoning by integrating Large Language Models (LLMs) with Symbolic AI (Knowledge Graphs, ontologies, and logic systems).

The goal is to build context‑aware, explainable, and factually grounded systems that leverage both “System 1” (fast, intuitive) and “System 2” (deliberate, logical) modes of reasoning.