Senior AI Solutions Engineer

Proclinical; England; On-site; GBP 80,000 - 110,000; Full time

Job summary

Proclinical seeks a Senior AI Solutions Engineer to lead design and development of AI-driven solutions that improve operational workflows and decision-making. You’ll build production-grade AI agents, assistants, and dashboards, enabling teams to move from manual processes to AI-enabled systems.

Collaborate with IT, architecture, data governance, and compliance to ensure secure, traceable implementations. You’ll work with Python, LLM APIs, and data platforms, guiding cross-functional teams and

Qualifications

• Strong experience designing production-grade AI solutions and agents.
• Proven ability to translate business needs into technical scopes for cross-functional teams.
• Background in regulated industries and data privacy requirements (GxP awareness).
• Master's or PhD in CS/AI or related field.

Responsibilities

• Design and implement AI solution architectures including agents, assistants, interfaces, and automations.
• Develop AI agents and dashboards using LLMs, RAG, and multi-agent workflows.
• Collaborate with Quality & Compliance to ensure validation and audit readiness.

Skills

Python

LLM integration

SQL

Communicating technical concepts

Project leadership

Regulated industries

R & Shiny

APIs and frameworks

Git / CI/CD

Cloud tech (AWS)

Education

Master's or PhD in Computer Science/AI

Tools

Snowflake

REST API

Docker

OpenAI

Anthropic Claude

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.