Senior AI Solutions Engineer

Proclinical; Welwyn; On-site; GBP 90,000 - 150,000; Full time

Job summary

Proclinical is expanding its AI capabilities and seeks a Senior AI Solutions Engineer to lead design and development of AI-driven workflows and dashboards. You will collaborate with IT, governance, and compliance to deliver scalable, secure AI solutions.

You will mentor engineers, set standards, and translate business needs into technical scopes across cross-functional teams, including data governance and security.

Qualifications

• Advanced degree in Computer Science or AI is required.
• Experience building production-grade AI agents and dashboards.
• Strong proficiency with Python and modern ML tooling.

Responsibilities

• Design and implement AI solution architectures with agents and automations.
• Develop AI agents using LLMs, RAG, and multi-agent workflows.
• Build NL interfaces and dashboards for data analysis.
• Create reusable AI component libraries for cross-team use.
• Ensure regulated AI use with compliance teams and auditability.
• Set coding standards, mentor engineers, and support business teams.
• Translate business needs into technical scopes with cross-functional partners.

Skills

Python

LLM APIs

SQL

AWS

Git CI/CD

Dashboard design

Education

Master's or PhD in Computer Science or AI

Tools

FastAPI

Pydantic

asyncio

pandas

pytest

OpenAI API

Anthropic Claude

LangGraph

CrewAI

Snowflake

REST API

Git

CI/CD

Docker

AWS

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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