Senior Software Engineer – Neuro-Symbolic AI & LLMs

About the Role

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.

About Project C3AN

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.