Experience
: 6.00 + years
Salary
: USD 3216-6432 / month (based on experience)
Expected Notice Period
: 15 Days
Shift
: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type
: Remote
Placement Type
: Full Time Contract for 12 Months(40 hrs a week/160 hrs a month)
(*Note: This is a requirement for one of Uplers' client - AI industry)What do you need for this opportunity?Must have skills required:PyTorch, Convolutional Neural Networks, Deep Learning, graph neural networks, Neurosymbolic AIAI industry is Looking for:
Senior Graph Intelligence Architect
Contract Duration: 12 Months
About Company
This company is pioneering the next generation of enterprise artificial general intelligence through revolutionary knowledgesculpting technology. We are building the critical infrastructure that enables organizations to transform their collectiveintelligence into precise, explainable, and trustworthy AI systems. Our platform bridges the gap between humanexpertise and machine reasoning, creating organizational AGI that operates with unprecedented accuracy andtransparency.We are at the intersection of knowledge graphs, neural reasoning, and enterprise AI - solving one of the mostimportant challenges in artificial intelligence today.
The Role
We are seeking a Senior Graph Intelligence Architect to lead the development of our neurosymbolic AI architecture.This is a founding technical role where you'll design and implement the graph neural network infrastructure thatpowers our knowledge reasoning engine. You'll be working at the cutting edge of graph-based machine learning,logical neural networks, and knowledge representation.This role is perfect for someone who believes that the future of AGI lies not just in scale, but in the intelligentcombination of structured knowledge and neural reasoning.
Core Responsibilities
Graph Neural Architecture Development Design and implement graph neural network architectures for knowledge reasoning over enterprise-scaleknowledge graphs (millions of nodes/edges) Develop novel approaches for combining Graph Convolutional Networks (GCNs), Graph Attention Networks(GATs), and Message Passing Neural Networks for ontological reasoning Build systems that can perform multi-hop reasoning and inference over complex organizational knowledgestructuresNeurosymbolic Integration Implement Logical Neural Networks (LNNs) or similar architectures that combine symbolic reasoning withneural learning Design hybrid systems that maintain explainability while leveraging deep learning capabilities Create bidirectional bridges between formal ontologies (OWL/RDF) and neural representationsKnowledge Graph Intelligence Develop graph embedding techniques optimized for enterprise knowledge representation Implement knowledge graph completion and link prediction algorithms Design temporal graph networks for handling evolving organizational knowledge Build graph-based anomaly detection for identifying knowledge conflicts and gaps
Scalable Graph Computing
Architect distributed graph processing systems handling billions of edges Optimize GNN training and inference for production environments Implement efficient graph sampling and batching strategies for large-scale graphs Design real-time graph update mechanisms for continuous knowledge evolutionResearch & Innovation Stay current with latest advances in geometric deep learning and neurosymbolic AI Contribute to research papers and patents in graph-based AGI Evaluate and integrate cutting-edge approaches from papers into production systems
Required Qualifications
Educational Background
PhD in Computer Science, Machine Learning, or related field with focus on graph neural networks,knowledge graphs, or neurosymbolic AI OR MS with 5+ years of exceptional industry experience in graph-based machine learning
Technical Expertise
Graph Neural Networks
Deep expertise in GNN architectures (GCN, GAT, GraphSAGE, GIN, PNA) Experience with geometric deep learning frameworks (PyTorch Geometric, DGL, Spektral) Knowledge of graph pooling, graph generation, and graph-to-graph translationKnowledge Graphs & Reasoning Hands-on experience with graph databases (Neo4j, TigerGraph, Amazon Neptune, or similar) Understanding of ontologies, RDF, OWL, and SPARQL Experience with reasoning engines and rule-based systems Knowledge graph embedding methods (TransE, ComplEx, RotatE, etc.)Neural Network Architectures Experience with Logical Neural Networks, Neural Theorem Provers, or similar neurosymbolic approaches Understanding of attention mechanisms and transformers for graphs Experience with CNN architectures adapted for graph-structured dataProgramming & Tools Expert-level Python with deep learning frameworks (PyTorch preferred) Experience with distributed computing (Ray, Spark, or similar) Graph processing frameworks (NetworkX, igraph, RAPIDS cuGraph) Production ML deployment (MLflow, Kubeflow, or similar)
Preferred Qualifications
Publications in top-tier conferences (NeurIPS, ICML, ICLR, KDD, WWW) Experience with enterprise knowledge management systems Background in formal logic, description logic, or automated reasoning Experience with large language models and their integration with knowledge graphs Contributions to open-source graph ML projects Experience with GPU-accelerated graph algorithms
How to apply for this opportunity?
- Step 1: Click On Apply! And Register or Login on our portal.
- Step 2: Complete the Screening Form & Upload updated Resume
- Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
About Uplers:
Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!
