Senior AI Solutions Engineer

Randstad Technologies Recruitment; City Of London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Randstad Technologies Recruitment is seeking a technical lead to architect and drive the organization’s enterprise AI roadmap. You will transition teams to automated pipelines, production-grade AI agents, and agentified dashboards, delivering scalable, compliant AI solutions.

You will lead the design of reusable AI foundations, guide cross-functional teams, and mentor engineers in MLOps, CI/CD, and observability. A Master’s/PhD in CS/AI is preferred, with strong Python and cloud skills.

Qualifications

• 4+ years in software, data, or ML engineering with 2+ years building production LLM apps, RAG, or autonomous agents.
• Expert-level production Python (FastAPI, Pydantic, asyncio, pytest, packaging).
• Hands-on experience with agent frameworks (LangGraph, AutoGen, CrewAI or native SDKs).
• Advanced SQL and cloud data warehouse proficiency (Snowflake preferred); Docker, Git, CI/CD, AWS deployment.

Responsibilities

• Design scalable enterprise AI architectures and multi-agent frameworks.
• Build and deploy agents with tool calling, structured outputs, and autonomous coordination.
• Develop interactive dashboards over portfolio, resourcing, and operational metrics (R Shiny or Python).
• Build and govern reusable AI skills, connectors, and evaluation benchmarks.
• Partner with Quality & Compliance to establish validation pathways and audit trails.
• Establish CI/CD, testing, containerization, and observability standards; mentor engineers.

Skills

Cross-functional communication

Production ML engineering

Python development

Data & cloud skills

Education

Advanced degree in CS/AI or quantitative field

Tools

Python

SQL

Snowflake

Docker

Git

CI/CD

AWS

R Shiny

Job description

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

• 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.