Senior AI Solutions Engineer

Proclinical

Schweiz

On-site

CHF 140,000 - 210,000

Full time

33 hours ago
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Job summary

Proclinical seeks a Senior AI Solutions Engineer to design production-grade AI agents, dashboards, and automation for enterprise decision-making. You will work with IT, governance, and compliance to build secure, scalable AI-enabled workflows.

You will lead cross‑functional teams, translate business needs into technical scopes, and mentor engineers while advancing AI adoption across regulated environments.

Qualifications

  • Master's or PhD in Computer Science, AI, or related field.
  • Experience designing production AI solutions for regulated environments.
  • Proficiency with Python, LLMs, and data workflows.

Responsibilities

  • Design AI solution architectures with agents, assistants, and dashboards.
  • Develop AI applications using LLMs, RAG, and multi-agent workflows.
  • Build NL interfaces and dashboards for portfolio, resourcing, and performance data.
  • Create reusable AI components for cross-team use.
  • Ensure regulated AI use cases meet validation and audit requirements.

Skills

Production-grade Python
LLM APIs
R Shiny
Cross-functional leadership
Communication

Education

Master's degree in Computer Science or AI

Tools

LangGraph
CrewAI
Snowflake
REST APIs
Docker
AWS
Git

Job description

Passionate about creating AI agents and assistants that drive smarter decision-making across organisations?

Proclinical is seeking a Senior AI Solutions Engineer to lead the design and development of AI-driven solutions that enhance operational workflows and decision-making processes. This role focuses on creating production-grade AI agents, assistants, and dashboards that empower teams to transition from manual processes to AI-enabled systems. You will collaborate with cross-functional teams, including IT, architecture, data governance, and compliance, to ensure secure, traceable, and compliant solutions.

Responsibilities:
  • Design and implement AI solution architectures, including agents, assistants, interfaces, and automations.
  • Develop AI agents and assistants using large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent workflows.
  • Build natural-language interfaces and dashboards for portfolio, resourcing, and performance data analysis.
  • Establish and maintain a shared library of reusable AI components for cross-team use.
  • Collaborate with Quality & Compliance teams to ensure regulated AI use cases meet validation and audit requirements.
  • Define test sets, monitor production quality, and maintain documentation and operational runbooks.
  • Set coding standards, review practices, and mentor engineers while supporting business teams in adopting AI solutions.
  • Translate business needs into technical scopes and partner with IT, architecture, and security teams to ensure platform access and data privacy.
Key Skills and Requirements:
  • Proficiency in production-quality Python (e.g., FastAPI, Pydantic, asyncio, pandas, pytest).
  • Experience with R and R Shiny for building and deploying applications.
  • Hands‑on expertise with LLM APIs (e.g., OpenAI, Anthropic Claude) and agent frameworks (e.g., LangGraph, CrewAI).
  • Advanced SQL skills and familiarity with Snowflake and REST API integrations.
  • Knowledge of engineering practices such as Git, CI/CD, Docker, AWS, and observability tools.
  • Understanding of GxP compliance, computer system validation, and data privacy in regulated industries.
  • Strong communication skills to translate technical concepts for non‑technical stakeholders.
  • Ability to prioritize tasks, manage risks, and lead technical project execution.
  • Advanced degree (Master's or PhD) in Computer Science, AI, or related fields.
  • Experience with in‑platform AI services in cloud data warehouses.
  • Familiarity with end‑to‑end security for AI workflows, including permissions and prompt injection protection.
  • Background in classical machine learning techniques (e.g., scikit‑learn for trend analysis and forecasting).
  • Experience in pharmaceutical R&D or other highly regulated industries.
  • A portfolio or GitHub profile showcasing relevant work is a plus.
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