Artificial Intelligence Engineer

Neuraci Labs is hiring an Artificial Intelligence Engineer to research, develop, train, and deploy proprietary foundation models and predictive systems. The role focuses on advanced deep learning, generative AI, knowledge graphs and retrieval systems, and scalable production ML infrastructure.

Responsibilities:

• Design, implement, train, and optimize neural architectures, including Graph Neural Networks (GNNs), Graph Transformers, CNNs, and attention-based Transformers
• Develop multimodal representation learning frameworks integrating structured, unstructured, relational, and high-dimensional datasets
• Implement self-supervised, contrastive, transfer, and multi-task learning approaches
• Explore geometric deep learning, equivariant neural networks, and domain-specific foundation model architectures
• Research and implement generative architectures, including diffusion models, score-based generative models, VAEs, and generative Transformers
• Develop predictive and generative models for complex structured data, graphs, and continuous representations
• Work with sparse, noisy, and heterogeneous datasets, addressing generalization, uncertainty, and data limitations
• Explore probabilistic modeling, causal inference, and uncertainty-aware machine learning
• Design knowledge graph architectures and graph-based representation learning pipelines
• Develop Retrieval-Augmented Generation (RAG), GraphRAG, hybrid retrieval, semantic search, and reranking systems
• Integrate LLMs, embeddings, vector databases, and structured knowledge for evidence-grounded reasoning
• Implement retrieval evaluation, source attribution, factual consistency checks, and hallucination mitigation
• Build scalable training pipelines using PyTorch and GPU-accelerated computing
• Implement custom loss functions, optimization techniques, and distributed training strategies
• Establish rigorous model evaluation frameworks, including ablation studies, cross-validation, calibration, and out-of-distribution testing
• Optimize model performance, memory utilization, training efficiency, and inference latency
• Develop reproducible ML pipelines, experiment tracking, model versioning, and production inference systems
• Build scalable model-serving APIs and integrate trained models into larger AI platforms
• Implement research papers, benchmark alternative architectures, and document experimental findings
• Collaborate with interdisciplinary researchers and engineers to develop proprietary AI capabilities