• Own the engineering enablement track of the AI Adoption Program • Cascade CTO's vision into engineering teams • Validate direction and risks of working practices • Run bi-weekly validation calls with active teams • Teach teams to define objective, numeric success criteria • Collaborate with cloud team on cost optimization and AWS credits • Capture reusable knowledge in a group knowledge base

Role Description Develop and fine-tune LLMs and NLP models using Hugging Face Transformers, OpenAI APIs, and LangChain. - Implement retrieval-augmented generation (RAG) for intelligent AI-driven question answering and chatbot applications. - Build and deploy AI models using FastAPI, Flask, and cloud-based inference engines (Azure ML, AWS SageMaker, GCP AI Platform). - Optimize embedding search and vector retrieval using FAISS, Pinecone, and ANN-based search algorithms. - Work on AI model deployment, API integration, and real-time AI application development in production environments. Qualifications - Bachelor's or Master's degree in Artificial Intelligence, Computer Science, Data Science, or a related field. - At least 2-4 years of experience in AI/ML development, NLP, or LLM-based application engineering. - Proficiency in Python, with experience in AI/ML frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. - Strong experience with OpenAI APIs, LangChain, and fine-tuning LLMs for domain-specific applications. - Expertise in developing and deploying AI-powered applications using FastAPI, Flask, or Django. - Hands-on experience with cloud-based AI services such as Azure ML, AWS SageMaker, or GCP AI Platform. - Knowledge of retrieval-augmented generation (RAG) and its implementation for AI-driven automation. - Proficiency in working with vector databases like FAISS, Pinecone, and ChromaDB for efficient search and retrieval. - Familiarity with containerization and orchestration tools such as Docker and Kubernetes for scalable AI deployment. - Strong problem-solving skills with the ability to troubleshoot and optimize AI models for real-world performance. - Excellent communication and teamwork skills to collaborate with AI researchers, engineers, and business teams. Company Description

• Design and implement end-to-end AI architecture for a new product within Tech Risk, focused on SOC reporting and intelligent automation. • Define and govern AI models, pipelines, and infrastructure, ensuring scalability, security, and compliance with enterprise and regulatory standards. • Drive innovation by incorporating LLMs, generative AI, and advanced analytics into product capabilities. • Collaborate with cross-functional teams to translate business requirements into AI-driven solutions. • Architect end-to-end AI platforms across cloud environments (Azure, AWS, GCP).

• Help architect intelligent solutions that combine Generative AI, automation, and data to solve real business challenges across multiple industries • Work alongside experienced consultants, engineers, and AI specialists, gaining hands-on experience designing prototypes, building AI workflows, and exploring the next generation of intelligent systems • Lead a team of junior AI consultants or interns in executing the project's prioritized in the backlog • Design and develop AI agents, assistants, and intelligent workflows that address real business needs • Support the automation of internal and client processes using Generative AI technologies and modern AI platforms • Explore emerging AI capabilities, including LLMs, copilots, automation frameworks, and intelligent orchestration tools • Collaborate with Data, AI, and business teams to translate opportunities into practical technical solutions • Build prototypes, proofs of concept (PoCs), and demonstrations that validate new AI use cases • Contribute to documenting architectures, learnings, implementation patterns, and reusable best practices • Analyze business processes and identify opportunities where AI can improve productivity and operational efficiency • Participate in innovation initiatives that evaluate new models, tools, and AI capabilities before they reach production • Develop a consulting mindset by combining technical experimentation with measurable business outcomes