Principal AI Engineer
Blend is an award-winning pure play data consultancy who help people do data right through project delivery across strategy and consulting, data science and BI, and data engineering. As a trusted Data & AI partner we co-create value with clients across a wide variety of industries. Our company has made the Inc. 5000 list of Fastest Growing Companies and currently have offices in Edinburgh, the US, Uruguay, and India. We are an accredited “Great Place To Work” company across all our office locations, with a shared and active focus on DEI initiatives and championing representation in all aspects of our work.
By combining our teams’ expert technical knowledge with a practical approach to value creation, we deliver outcomes that make a real change for our clients. From using computer vision to remotely monitor crops to implementing a BI dashboard to help swimmers win more medals - nothing we do is designed to be left on the shelf.
About the role
You’ll be one of the senior technical leaders responsible for the direction, quality and growth of AI Engineering at Blend360.
This is a broad leadership role spanning technical strategy, major client engagements, engineering standards and the development of our AI Engineering capability. You’ll operate across the department, providing leadership wherever the biggest technical decisions, risks or opportunities sit.
You’ll also remain deeply hands-on. You’ll design architectures, challenge technical decisions, work directly with engineers and clients, and get into the code when the problem warrants it.
You’ll be expected to challenge technical decisions where needed, explain your reasoning clearly and help teams arrive at stronger solutions. When a client or internal team proposes an approach that won’t hold up, you’ll be able to identify the risks, make the case for a better option and take responsibility for the technical direction.
We’re not looking for someone to simply review or approve other people’s architecture. You’ll be expected to set technical direction, make difficult decisions and remain accountable for the quality of what we deliver.
Our AI Engineering work spans CPG, pharma and energy clients, and it’s growing.
The work
Set technical direction across AI Engineering, defining the architecture principles, engineering standards, delivery practices and technical capabilities we need as the practice grows.
Own the technical quality of major AI engagements, particularly where architecture, scale, complexity or delivery risk requires senior leadership.
Lead across multiple projects and technical workstreams, setting priorities and direction while ensuring teams can execute without becoming dependent on you for every decision.
Design and review production AI architectures, including retrieval and knowledge layers, agentic pipelines, evaluation, multilingual systems, and the cost, latency, reliability and scalability trade-offs involved.
Remain hands-on on the hardest problems: reviewing code, prototyping approaches, resolving architectural issues and working directly with engineers when senior technical intervention will materially improve the outcome.
Run rigorous design reviews that raise the engineering bar across the practice and create an environment where technical decisions are challenged regardless of seniority.
Act as a senior technical counterpart to clients, including CxO and architecture leadership, taking ownership of difficult technical conversations, trade-offs, delivery risks and changes in direction.
Own the technical quality of major AI proposals, translating solution concepts into credible architectures, scopes, delivery models, team structures, estimates and commercial assumptions.
Work with commercial and account leadership to shape technical propositions, identify opportunities and determine where the AI Engineering practice should invest and differentiate.
Develop senior engineers and technical leads, building the leadership depth and succession required to scale the department.
Shape the AI Engineering capability plan, including hiring priorities, skills development, team composition and the bar for senior technical talent.
At least 10 years’ experience across AI, data and software engineering, including 3+ years leading engineering teams or a substantial technical function within consulting or professional services.
Experience operating beyond individual project leadership, with responsibility for technical direction, engineering quality or capability across multiple teams.
Deep technical credibility. You’re comfortable working in production Python, substantial codebases and API-driven systems that need to perform reliably at scale.
Recent, personal experience architecting and building production AI systems. Expect to discuss retrieval strategy, caching, context economics, serving constraints, evaluation, observability and what went wrong in practice.
Strong systems thinking. You can reason through unfamiliar platforms and problems rather than relying on expertise in a single stack.
Experience leading complex programmes or multiple concurrent engineering workstreams, with accountability for technical direction, planning, resourcing, risk and delivery outcomes.
The judgement to know when to intervene personally and when to lead through others, delegating effectively without giving up accountability for technical quality.
Experience developing senior engineers and technical leaders, shaping team capability and raising the engineering bar across a wider organisation.
Commercial awareness sufficient to turn a technical solution into a realistic scope, team shape, estimate and delivery plan, and to challenge assumptions that do not hold up.
Experience contributing to account growth, technical propositions or go-to-market activity within a consulting organisation.
Confidence operating with senior clients and executives while remaining credible with engineers at code and architecture level.
The ability to make difficult technical calls, create clarity where there is ambiguity and take responsibility for the outcome.
Strong experience with Databricks and Azure OpenAI, which underpin much of our delivery.
Nice to have
Ontology, knowledge graph or semantic layer experience.
Delivery experience in pharma or CPG.
Practical experience designing systems around EU AI Act requirements.
Multilingual AI systems in production.
A strong presence in the Databricks or Microsoft partner ecosystem.
Experience shaping go-to-market and commercial strategy for an AI Engineering practice.