Software Engineer

Required Qualifications

• Education:
• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
• Experience:
• 2+ years of hands-on experience in software development, particularly in Generative AI (e.g., GPT, BERT, transformer models, GANs).
• 1+ years of experience working with Knowledge Graphs (e.g., GraphQL, RDF, OWL, SPARQL, Neo4j, or other graph databases).
• Solid understanding of Natural Language Processing (NLP) techniques and frameworks.
• Experience with cloud platforms (AWS, GCP, Azure) and deployment of AI systems.
• Skills:
• Strong proficiency in Python, Java, or C++ (with experience in TensorFlow, PyTorch, or similar AI/ML frameworks).
• Experience in building and optimizing Graph Databases and GraphQL APIs.
• Solid understanding of algorithms, data structures, and performance optimization techniques for large-scale AI systems.
• Familiarity with DevOps tools and CI/CD pipelines for deployment automation.
• Additional Skills:
• Strong analytical, problem-solving, and debugging skills.
• Ability to work in a collaborative and agile environment.
• Good communication skills, both written and verbal.
• Ability to translate complex technical concepts into clear business implications.

Key Responsibilities

• Software Development: Develop and optimize scalable, robust, and high-performance software solutions that leverage Generative AI and Knowledge Graphs.
• Generative AI Integration: Work with AI models such as GPT, transformers, and other generative approaches to build advanced natural language generation, summarization, and conversational AI systems.
• Knowledge Graph Design: Design, implement, and maintain scalable knowledge graphs and semantic networks that capture structured and unstructured data for enhanced decision-making.
• Data Engineering: Collaborate with data scientists and engineers to prepare and process large datasets to train AI models and build knowledge graphs.
• Research & Innovation: Stay up-to-date with the latest advancements in AI, particularly in the areas of knowledge graphs, natural language understanding (NLU), and generative modeling. Apply cutting-edge research to improve product capabilities.
• Cross-Functional Collaboration: Work closely with product managers, researchers, and other engineers to translate business requirements into technical solutions.
• Performance Tuning & Optimization: Continuously improve the efficiency and accuracy of AI models and the knowledge graph infrastructure, ensuring minimal latency and high throughput.
• Documentation & Reporting: Maintain clear documentation of system architectures, model performance, and development processes to facilitate team collaboration and future updates.

Regards,

Shashvat Desai (Shasha)

+40316214029

[email protected]