Senior AI Lead with LLM and RAG

Role overview Shape an enterprise AI practice that connects responsible adoption to measurable business outcomes. As the primary AI advisor, you’ll help identify and prioritize opportunities, guide initiatives from strategy through delivery, and build the governance, skills, and working practices needed to sustain AI use across business units.

Responsibilities - Develop an AI strategy and roadmap aligned with business goals, assessing and prioritizing opportunities across teams. - Advise product, engineering, data science, and business stakeholders on solution design using technologies such as LLMs, retrieval-augmented generation, agents, and MLOps. - Guide AI initiatives from early assessment through implementation, maintaining technical quality and responsible-use principles. - Establish governance, usage policies, and safeguards, while communicating delivery progress, risks, outcomes, and expected value. - Define measures for adoption and return on investment; run workshops, demonstrations, and enablement activities. - Mentor team members and guide the adoption of AI-supported and agent-orchestrated software development practices.

Requirements - At least seven years in AI consulting, solution architecture, or a related role with leadership responsibilities. - Experience delivering enterprise AI initiatives from ideation through design and implementation. - Knowledge of AI and machine learning areas such as LLMs, NLP, computer vision, conversational AI, RAG, and MLOps. - Ability to translate business needs for clients and senior leaders into practical AI solutions. - Hands-on collaboration with development and data science teams using tools such as Python, PyTorch or TensorFlow, Jupyter, Git, and CI/CD pipelines. - Understanding of AI ethics, data privacy, governance, and responsible-use practices, with experience enabling adoption.

Nice to have - An advanced degree in computer science, AI, data science, or a related field, or equivalent professional experience. - Experience with one or more cloud AI platforms, lifecycle and MLOps tools, agent orchestration frameworks, or vector search systems.