Artificial Intelligence Engineer

AI Engineer — Client DeliveryROLE OVERVIEWWe are seeking an experienced AI Engineer to join our consulting practice, responsible for designing, building, and deploying AI and agentic systems directly within client environments. This is a hands-on engineering role with a client-facing dimension: beyond building, you will scope technical requirements with clients, present architecture and progress to stakeholders, and lead the technical workstream on engagements. The ideal candidate pairs deep, current hands-on experience in agentic AI engineering with the maturity to represent the team in front of clients.KEY RESPONSIBILITIESDesign, build, and deploy AI and agentic systems for clients using agent frameworks (e.g., AWS Bedrock, LangChain), LLMs, RAG, and prompt engineering.Serve as a technical point of contact for clients: scoping requirements, presenting architecture and solution designs, and communicating progress and trade-offs directly to client stakeholders.Lead the technical workstream on engagements, guiding architecture decisions and unblocking delivery.Engineer retrieval systems and vector database architectures optimized for production AI applications.Build and own LLM evaluation frameworks to assess model and system performance and drive iterative improvement.Design and integrate APIs, identity and access management, and platform integrations so AI systems operate securely within client environments.Collaborate with cross-functional teams to evaluate and refine AI models, ensuring production-ready standards.Ensure successful deployment of AI systems in production, with a focus on scalability, reliability, and security.REQUIREMENTSProficiency in Python and hands-on experience building production AI systems.Experience building and shipping production features or product iterations, within production standards, on top of AI models within an enterprise environment.Direct, hands-on experience building with agent frameworks (e.g., AWS Bedrock, LangChain, or similar).Experience building AI in complex or regulated environments with controls over CI/CD pipelines and developer workspaces; or alternatively, experience owning or managing developer workspaces.Experience with LLMs, including model selection, fine-tuning, prompt engineering, and deployment.Retrieval engineering experience: vector databases, retrieval architecture, and RAG systems in production.Experience building or owning LLM evaluation frameworks and metrics.Familiarity with identity and access management as it applies to AI platforms and agentic systems.Comfortable speaking directly with clients, able to explain technical architecture and trade-offs to both technical and business stakeholders.Experience designing and integrating APIs to connect AI systems with other platforms.QUALIFICATIONS & SKILLSExperience: 8+ years combined experience in management consulting, technology advisory, or enterprise AI/software product delivery, often with leadership track records.Core Fluency: Equal command of commercial strategy (go-to-market, P&L ownership, pricing) and technical depth (LLMs, cloud ecosystems like Snowflake, AWS, or Azure, agentic architectures, and data foundations).Communication: Exceptional executive-level communication and storytelling skills to present complex AI concepts clearly to both technical developers and non-technical C-suite stakeholders.Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Business, or a related quantitative field.NICE TO HAVESnowflake AI experience.Broader AI platform engineering experience, MLOps, model deployment pipelines, or platform infrastructure.Familiarity with emerging agentic architectures, new LLM capabilities, or innovative RAG approaches.Prior consulting or client-facing delivery experience, including presenting to client stakeholders or leading a technical workstream.Experience with agile development methodologies and version control systems.Strong collaboration and communication skills.