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., 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.

• 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 forecasting) and a strong GitHub/project portfolio.