Senior AI Solutions Engineer Maanger (Technical Specialist - AI/Python/R Shiny/Snowlfake/LLM)
This is a hands-on technical managerial role where you will be leading the AI function on an end to end basis at an enterprise level.
As a Senior AI Solutions Engineer you are the Technical Lead lead for my client's AI solutions, reporting to the Business Digital, Data & AI Infrastructure Lead. You will design and build the AI agents, assistants and agentified dashboards/cockpits that let BSO move from manual assembly to AI-enabled workflows, and set the patterns, standards and shared components that others reuse.
You will be part of Business Digital, Data & AI Infrastructure sub-function, which designs, builds and runs the data, digital and AI capabilities used across my client You will work closely with business teams, IT & Architecture, Data Governance, Information Security and Quality & Compliance.
You will turn business needs into production-grade solutions that are secure, traceable and, where required, GxP-compliant - taking my client step by step from AI assistants to supervised and orchestrated agents.
Essential Technical Skills
Experience: At least 4 years in software, data or machine-learning engineering, including 2+ years building LLM-based applications or agents that are used in production.
Project Management: Led technical project execution for complex AI/ML engagements, translating business requirements into scalable cloud architectures and managing timelines, risks, and deployment roadmaps across multiple enterprise accounts .
Python: Expert, production-quality Python (for example FastAPI, Pydantic, asyncio, pandas, pytest).
R and R Shiny: Solid R and experience building and deploying Shiny applications.
LLM and agent engineering: Hands-on experience with LLM APIs (Anthropic Claude, OpenAI or similar), tool calling and connector patterns, RAG and vector search, agent frameworks (for example LangGraph, CrewAI or AutoGen) and LLM evaluation.
Data and integration: Advanced SQL and working proficiency with Snowflake; integrating enterprise systems through REST APIs.
Engineering practices: Git, CI/CD, containerisation (Docker), cloud services (AWS) and observability (logging, tracing, metrics).
Regulated environments: Understanding of GxP, computer system validation, data privacy and reproducibility for AI in a regulated industry.
Communication: Explains complex technical choices in plain language to non-technical stakeholders and turns loosely described needs into clear technical scope.
You are a strategic, hands-on engineer who thrives where software engineering, AI orchestration and real business impact meet. You value reliability and reproducibility, and you explain technical choices clearly to colleagues across BSO.
Education: Bachelor's or advanced degree in Computer Science, Engineering, Data Science or a related field.
Preferred Qualifications
Master's or PhD in Computer Science, AI or a related quantitative field.
In-platform AI services in cloud data warehouses.
End-to-end security of agentic workflows (permissions, data access and protection against prompt injection).
Experience with validated quality management or other regulated enterprise systems.
Classical machine learning (for example scikit-learn) for trend analysis, clustering and forecasting.
Experience in pharmaceutical R&D or another highly regulated industry.
A GitHub profile or portfolio showing relevant work is an advantage.
Key Responsibilities
AI solution architecture: Own the technical design AI solutions - agents, assistants, AI interfaces and automations. Favour adopting and configuring existing capabilities before building, and design for reuse across teams.
Agentic AI and LLM engineering: Build agents and assistants on enterprise large language models: tool calling, connectors to business systems, retrieval-augmented generation (RAG) over procedural and quality documents, and prompt and context engineering, including multi-agent workflows where specialised agents plan, execute and check the work.
AI interfaces over business data: Build natural-language interfaces and agentified dashboards over clien'ts portfolio, resourcing and performance data, so leaders can ask questions, see trends and act on alerts.
Shared foundation: Build and govern a shared library of reusable AI skills and components, so a capability built once is used across the client
GxP and trust by design: Partner with Quality & Compliance on the validation path for regulated AI use cases: human-in-the-loop decisions, audit trails, reproducibility, guardrails and computer system validation evidence.
Evaluation and operations: Define test sets and accuracy measures, monitor quality, cost and usage in production, and maintain documentation and runbooks.
Technical leadership: Set coding and review standards, CI/CD and version-control practices; mentor engineers and support the business colleagues who adopt and champion AI in their teams.
Stakeholder partnership: Translate business needs into technical scope, and work with IT & Architecture, Information Security, Data Governance and PDD AI experts on platform, access and privacy decisions.The role can be based Hybrid working out of Welwyn Garden City with a view to be on site ⅔ days a week. This is an excellent opportunity and I have interview slots ready to be filled so don't delay and apply ASAP to be considered.
Randstad Technologies is acting as an Employment Business in relation to this vacancy
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