AI Engineer, Forward Deployed

Mission

Join the Engineering team as a Forward Deployed AI Engineer — a hybrid role combining the skills of an engineer, solutions architect, and consultant. This position is designed to embed directly with internal operational teams (beginning with Managed Services) to develop, implement, customize, and troubleshoot AI models in real‑world production environments. You will serve as the critical bridge between our product team and internal operational departments, translating cutting‑edge AI capabilities into practical, measurable outcomes for the business.

The ideal candidate thrives in ambiguous, fast‑moving environments and is equally comfortable writing production code, advising stakeholders, and redesigning workflows around AI‑first thinking.

Act as a resident AI expert within Individual Departments/Business Units (e.g. Managed Services) and other internal teams, understanding their workflows end‑to‑end and identifying where AI can drive efficiency, accuracy, and scale.

Function as engineer, solutions architect, and internal consultant — designing solutions, building them, and guiding teams through adoption and change management.

Design and build AI‑powered application features using LLM APIs, tool‑calling patterns, and modern coding tools.

Create agent loops that can select tools, execute actions, summarize results, and produce traceable user responses.

Develop chat‑based analytical experiences that connect user questions to backend tools, data services, semantic models, and visualization outputs.

Improve prompt quality, reduce token usage, manage context windows, and optimize model/API cost without degrading output quality.

Use advanced AI coding tools such as Cursor, Claude, Codex, or similar tools to accelerate development while maintaining code quality and review discipline.

• Embed directly with internal departments to understand day‑to‑day workflows, pain points, and operational bottlenecks where AI can have the highest impact.
• Act as the on‑the‑ground AI expert — participating in team standups, process reviews, and strategic planning sessions to continuously surface AI opportunities.
• Translate operational needs into AI solution requirements, bridging communication between the product engineering team and internal stakeholders.
• Lead the end‑to‑end implementation of AI solutions within operational contexts: from scoping and design through build, testing, deployment, and iteration.
• Provide hands‑on troubleshooting and support for AI models running in production within internal team environments, ensuring reliability and performance.
• Drive change management and adoption by training internal team members on new AI tools, workflows, and best practices.
• Document operational AI use cases, implementation patterns, and lessons learned to inform the product roadmap and support scaling to additional teams.

• Design, build, and maintain LLM‑powered features for enterprise data applications, including natural‑language analytics and AI‑assisted workflows.
• Implement agent loops that support multi‑step reasoning, tool‑calling, retry handling, tool‑result summarization, and final response generation.
• Define and maintain tool schemas for LLM tool‑calling, including tool names, descriptions, required inputs, output contracts, and safe execution boundaries.
• Build orchestration logic that maps LLM tool requests to backend functions, executes the tools, handles errors, and feeds summarized results back into the conversation.
• Create traceable AI experiences where users can inspect tool steps, generated SQL, data outputs, chart recommendations, and final explanations.

• Develop domain‑aware system prompts, instruction templates, and response formats tailored to Managed Services workflows and broader pharmaceutical data analytics use cases.
• Optimize prompts…