Senior AI Solutions Engineer

Based within Product Development Data Sciences (PDD) , you will serve as the technical lead and architect driving the organization's enterprise AI roadmap. You will transition operational teams from manual data handling to intelligent, automated steering through production-grade AI agents, agentified dashboards, and automated data pipelines. Key Responsibilities Here is an overview of the core

responsibilities

AI Solution Architecture: Design reusable, scalable enterprise AI architectures, multi-agent frameworks, and RAG pipelines across operational workflows. Agentic AI & LLMs: Build and deploy agents using modern foundation models (Claude, OpenAI) featuring tool calling, structured outputs, and autonomous multi-agent coordination.

Agentified Dashboards: Develop interactive, natural-language interfaces and dashboards (R Shiny or Python) over portfolio, resourcing, and operational metrics. Shared Foundations: Build and govern a centralized library of reusable AI skills, connectors, and evaluation benchmarks across the organization.

GxP & Trust by Design: Partner with Quality & Compliance to establish validation pathways, deterministic guardrails, human-in-the-loop controls, and audit trails. Technical Leadership & MLOps: Establish CI/CD, testing, containerization, and observability standards; mentor engineers and guide cross-functional teams on AI adoption.

Must-Haves Candidates must meet the following minimum criteria: Experience: 4+ years in software, data, or ML engineering, including 2+ years building production LLM apps, RAG, or autonomous agents. Python: Expert-level production Python (FastAPI, Pydantic, asyncio, pytest, packaging).

Agentic Frameworks: Hands-on experience with frameworks such as LangGraph, AutoGen, CrewAI, or native agent SDKs. Data & Cloud: Advanced SQL, cloud data warehouse proficiency (Snowflake preferred), Docker, Git, CI/CD, and AWS deployment.

Visualization: Solid experience with R Shiny or Shiny for Python. Governance & Mindset: Working knowledge of validation, auditability, data privacy, and security (GxP/regulated environment awareness) with strong cross-functional communication skills.

Preferred Qualifications The following qualifications are advantageous: Advanced degree (Master's or Ph.D.) in Computer Science, AI, or a quantitative discipline. Experience with in-database AI (e.g., Snowflake Cortex) and agent security/jailbreak defense.

Background in Pharmaceutical R&D, clinical development, or another heavily regulated sector. Classical ML experience (scikit-learn, time-series for...

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