Job
Description
As a highly skilled Data Scientist specializing in AI agents, generative AI, and knowledge engineering, you will play a crucial role in enhancing AI-driven cloud governance solutions. Your primary responsibilities will involve advancing multi-agent systems, leveraging LLMs, and integrating knowledge graphs in a Python environment. Your contributions will directly impact FinOps, SecOps, CloudOps, and DevOps by providing scalable, AI-enhanced decision-making, workflows, and monitoring. - **AI Agent Development & Enhancement**: - Design, develop, and optimize LLM-based multi-agent systems for cloud governance. - Implement agent collaboration using frameworks like LangChain, AutoGen, or open-source MAS architectures. - Develop adaptive AI workflows to improve governance, compliance, and cost optimization. - **Generative AI & Knowledge Engineering**: - Apply generative AI techniques such as GPT-4, Google Gemini, and fine-tuned BERT models to knowledge representation and reasoning. - Design and manage knowledge graphs, OWL ontologies, and SPARQL queries for intelligent decision-making. - Enhance AI agent knowledge retrieval using symbolic reasoning and semantic search. - **Machine Learning & NLP**: - Develop embedding-based search models for retrieving and classifying cloud governance documents. - Fine-tune BERT, OpenAI embeddings, or custom transformer models for document classification and recommendation. - Integrate discrete event simulation (DES) or digital twins for adaptive cloud governance modeling. - **Cloud Governance & Automation**: - Work with multi-cloud environments (AWS, Azure, GCP, OCI) to extract, analyze, and manage structured/unstructured cloud data. - Implement AI-driven policy recommendations for FinOps, SecOps, and DevOps workflows. - Collaborate with CloudOps engineers and domain experts to enhance AI-driven automation and monitoring. **Required Qualifications**: - 4+ years of experience in Data Science, AI, or Knowledge Engineering. - Extensive knowledge or experience in Knowledge Engineering is preferred. - Strong proficiency in Python and relevant ML/AI libraries (PyTorch, TensorFlow, scikit-learn). - Hands-on experience with knowledge graphs, OWL ontologies, RDF, and SPARQL. - Expertise in LLMs, NLP, and embedding-based retrieval (OpenAI, Cohere, Hugging Face models). - Familiarity with multi-agent systems, LangChain, AutoGen, or similar frameworks. - Experience working with cloud platforms (AWS, Azure, GCP) and AI-driven cloud governance. In this role, you will have the opportunity to work on cutting-edge AI agent ecosystems for cloud governance in a collaborative environment that brings together AI, knowledge engineering, and cloud automation. Additionally, you can enjoy competitive compensation, benefits, and flexible work arrangements, including remote/hybrid options. Your success in this position will depend on your ability to thrive in a fast-paced environment, exhibit intellectual curiosity, and apply advanced AI techniques to address real-world cybersecurity challenges. As a highly skilled Data Scientist specializing in AI agents, generative AI, and knowledge engineering, you will play a crucial role in enhancing AI-driven cloud governance solutions. Your primary responsibilities will involve advancing multi-agent systems, leveraging LLMs, and integrating knowledge graphs in a Python environment. Your contributions will directly impact FinOps, SecOps, CloudOps, and DevOps by providing scalable, AI-enhanced decision-making, workflows, and monitoring. - **AI Agent Development & Enhancement**: - Design, develop, and optimize LLM-based multi-agent systems for cloud governance. - Implement agent collaboration using frameworks like LangChain, AutoGen, or open-source MAS architectures. - Develop adaptive AI workflows to improve governance, compliance, and cost optimization. - **Generative AI & Knowledge Engineering**: - Apply generative AI techniques such as GPT-4, Google Gemini, and fine-tuned BERT models to knowledge representation and reasoning. - Design and manage knowledge graphs, OWL ontologies, and SPARQL queries for intelligent decision-making. - Enhance AI agent knowledge retrieval using symbolic reasoning and semantic search. - **Machine Learning & NLP**: - Develop embedding-based search models for retrieving and classifying cloud governance documents. - Fine-tune BERT, OpenAI embeddings, or custom transformer models for document classification and recommendation. - Integrate discrete event simulation (DES) or digital twins for adaptive cloud governance modeling. - **Cloud Governance & Automation**: - Work with multi-cloud environments (AWS, Azure, GCP, OCI) to extract, analyze, and manage structured/unstructured cloud data. - Implement AI-driven policy recommendations for FinOps, SecOps, and DevOps workflows. - Collaborate with CloudOps engineers and domain experts to enhance AI-driven automation and monitoring. **Req
