Applied AI Engineer

About the Role
The Applied AI Engineer is the hands-on builder inside the AI Innovation Lab. You'll prototype, integrate, and refine AI-powered solutions that drive immediate business value — from internal copilots and workflow automations to retrieval-augmented assistants and LLM-driven analytics. You'll work side by side with business stakeholders, the Data MVP team, and our shared data
science function to turn promising ideas into working pilots — and then help transition successful pilots into hardened, production-grade solutions. This role is ideal for someone who loves to get under the hood of large language models, can wire up real data and APIs fast, and is comfortable iterating in the open with executives and end users.
Key Responsibilities
Solution Prototyping & Integration

• Build AI-powered prototypes and pilots, including chat/agent experiences, workflow automation, retrieval-augmented Q&A, and intelligent copilots for internal teams.
• Integrate LLMs and other AI services with enterprise systems (APIs, data warehouses, productivity tools, document stores, etc.).
• Stand up vector retrieval / embedding pipelines and lightweight orchestration layers for pilots (e.g. routing, guardrails, context injection).
• Own prompt engineering and system prompt design for pilot use cases; maintain prompt/version libraries for reuse across teams.

Iteration, Evaluation & Hardening

• Run small-scale evaluations: quality, latency, hallucination rate, cost per interaction, user satisfaction.
• Instrument prototypes to capture feedback and usage data; use that to drive iterative improvement.
• Work with the Data MVP / Platform team and shared Data Scientist to transition successful pilots to governed, supportable, auditable solutions.

AI Safety, Policy & Guardrails

• Implement privacy, security, and compliance controls defined by the Lab and CTO (e.g. redaction, role-based access, safe completion patterns).
• Help encode those guardrails directly into the pilots (model routing logic, input/output filters, content moderation triggers) so adoption can scale safely.

Internal Enablement

• Document how each pilot works: architecture diagrams, data flows, prompts, limitations, known risks, and "dos and don'ts.”
• Partner with AI Enablement / Adoption Specialists to teach business users how to work with new copilots and AI workflows in their day-to-day.

This position description outlines the basic tasks and requirements for the position noted. It is not a comprehensive listing of all job duties.

Qualifications:

• 4+ years of hands-on experience in software engineering, ML engineering, applied data science, or related technical role. (Startup / skunkworks / innovation lab experience is a plus.)
• Demonstrated experience building with large language models or similar advanced AI systems — e.g. using commercial LLM APIs, open-source models, orchestration frameworks, RAG pipelines, vector databases.
• Strong Python and/or TypeScript/JavaScript skills, including building API services, simple web front ends, and automation scripts.
• Practical familiarity with data platforms and modern data stacks (Snowflake or similar warehouse; dbt / transformation layer; cloud services), and comfort working with structured and unstructured data.
• Comfort working in ambiguous problem spaces: you can sit with a business stakeholder, map their manual workflow, and propose/build an AI-enabled version quickly.
• Understanding of AI governance, security, and responsible-use expectations in an enterprise setting; able to design with those constraints in mind.
• Strong written and verbal communication skills; able to explain what the pilot does, why it matters, and what its limits are — without jargon.
• Bachelor's degree in Computer Science, Data Science, Operations Research, Engineering, or related field. Graduate work a plus.