Partner with business stakeholders to identify, design, and deliver impactful AI solutions, including AI agents, chatbots, NLP models, and predictive analytics. Perform feasibility assessments and solution design for use cases involving machine learning, LLMs, retrieval-augmented generation (RAG), agent orchestration, and automation. Develop, test, and deploy AI models, pipelines, and integrations using Python, cloud services, and modern AI frameworks. Act as a technical mentor across AI initiatives, advising on model development, architecture, coding standards, and best practices. Collaborate with IT on infrastructure, data strategy, model hosting, security considerations, and integration with enterprise platforms. Conduct AI-specific risk assessments, including privacy, data protection, model bias, explainability, and responsible AI controls; partner with Legal and Cybersecurity to document risks and mitigation strategies. Participate in agile ceremonies such as sprint planning, backlog refinement, estimation, and daily standups. Ensure adherence to development standards, documentation requirements, testing protocols, and governance processes for AI solutions. Bachelor's degree required; Computer Science, Software Engineering, Data Science, or related field preferred. Experience with cloud platforms such as Azure and/or AWS, including AI/ML services (e.g., Azure OpenAI, Amazon Bedrock, SageMaker). Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, or similar). Strong understanding of machine learning, deep learning, and LLM-based applications. Proficiency in Python and modern development practices (version control, APIs, containers, CI/CD). Strong analytical, technical, critical thinking, and problem-solving skills with the ability to translate business needs into solution designs. Excellent verbal and written communication skills with the ability to articulate complex technical concepts. Demonstrated interpersonal skills and ability to collaborate across diverse teams. Ability to thrive in a dynamic environment and manage multiple priorities under tight timelines. English required; additional languages a plus. Experience designing or deploying generative AI solutions such as chatbots, AI agents, vector databases, or RAG pipelines. Experience with automation technologies (Automation Anywhere, Power Platform, OCR tools) and integrating AI into business workflows. Experience with Agile methodologies and tools such as Azure DevOps or Jira, including story sizing and backlog management. Exposure to global business environments, cross-functional teams, or shared services organizations. Familiarity with MLOps practices, model monitoring, and managing model drift. Experience working in regulated industries or environments requiring strong data governance and compliance controls.