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.
- 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.
- 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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