Senior Applied Artificial Intelligence (AI) Engineer

We are seeking a Senior Applied AI Engineer who is a true hands-on builder, someone who can take a business problem and quickly turn it into a working AI solution. This is not a coordination or oversight role. You will be expected to write code, build workflows, integrate systems, and deliver usable prototypes in days to weeks and production-ready increments through defined delivery sprints. This role is expected to be highly practical; the person should be able to work with incomplete requirements, imperfect data, enterprise constraints, and fast-changing priorities. This role will operate under the direction of the Artificial Intelligence Leader (Head of AI) and will support enterprise AI priorities across functions, including but not limited to engineering, procurement, supply chain, operations, legal, quality, and commercial teams You will work directly with messy enterprise data, APIs, and LLM technologies to create practical solutions such as document extraction workflows, copilots, and automation tools. The ideal candidate is fast, pragmatic, and execution-focused—someone who values working solutions over perfect designs and thrives in ambiguity. This role is key to accelerating time-to-value, validating vendor solutions, and proving what is possible through real delivery. General Responsibilities Design, develop, and deploy production models, services, and pipelines that are reliable, scalable, and maintainable. Partner with data science, product, data engineering, and platform teams to translate business problems into technical solutions. Build and optimize model training, evaluation, deployment, monitoring, and retraining workflows. Improve model performance, latency, cost efficiency, reliability, and explainability in production environments. Develop reusable infrastructure, tooling, libraries, and patterns that accelerate team delivery. Monitor deployed models for performance degradation, bias, drift, and operational issues; recommend and implement corrective actions. Collaborate with stakeholders to communicate technical trade-offs, risks, timelines, and recommendations clearly. Mentor engineers and contribute to technical standards, best practices, and architecture decisions. Stay current with advances in practices. Experience / Qualifications A university degree required (i.e. Bachelors degree) or equivalent relevant work experience. Must be a team player able to work in a fast-paced environment with demonstrated ability to handle multiple competing tasks and demands Strong communication skills; oral, written and presentation Strong organization, planning and time management skills to achieve results Strong personal and professional ethical values and integrity Holds self-accountable to achieving goals and standards Proficient in Microsoft Office programs (Outlook, Word, PowerPoint, and Excel) Strong interpersonal & collaboration skills to work effectively with all levels of the organization including suppliers and/or external customers Additional Responsibilities : Rapidly design, build, and deploy AI solutions, delivering usable prototypes and production-ready applications Develop end-to-end AI/LLM workflows (RAG, summarization, classification, agents) including APIs, data pipelines, and system integrations Build scalable solutions using Python, REST APIs, Azure OpenAI/OpenAI, vector databases, and orchestration frameworks Ingest, process, and work directly with messy, unstructured enterprise data (e.g., PDFs, Excel, SharePoint) Create user-facing tools and applications with appropriate interfaces, integrations, and deployment mechanisms Implement and manage evaluation, monitoring, and reliability frameworks (testing, scoring, human-in-the-loop, logging, error analysis) Evaluate, benchmark, and support due diligence of vendor solutions against internal builds Prioritize and deliver AI initiatives based on business value, scalability, and speed-to-impact across multiple enterprise use cases Operate effectively in ambiguous environments with evolving requirements and data constraints Additional Qualifications : 5–10+ years of hands-on experience in software, AI, or data engineering with strong Python proficiency for building production-ready solutions Experience designing and delivering end-to-end AI workflows and applications (beyond standalone models) Hands-on experience with LLM/GenAI technologies, including RAG architectures, vector embeddings/databases, and multi-step orchestration Experience with AI/LLM frameworks and tools (e.g., LangChain, LlamaIndex, Semantic Kernel, OpenAI/Azure OpenAI APIs) Experience working with messy, unstructured enterprise data and the ability to deliver effective solutions without perfect data or infrastructure Strong engineering and product judgment, including API/application development (FastAPI, Streamlit, etc.), secure development practices, and decision-making on prototype vs. production vs. vendor solutions Nice-to-have Experience with data handling and