Associate Director, AI Engineering

🏢 Blend360 · all Blend360 jobs
📍 United Kingdom
📅 Posted 2026-08-21 · via Himalayas
🏷 AI-Engineering,Machine-Learning-Engineering,Data-Science-Leadership,AI-DL-Infrastructure,Director-AI-Engineering,Director-Of-AI-Engineering,AI-Engineering-Director,AI-ML-Engineering-Director,Associate-Director-Of-Machine-Learning,Technology-Leadership
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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
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Set technical direction across AI Engineering, defining the architecture principles, engineering standards, delivery practices and technical capabilities we need as the practice grows.

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Own the technical quality of major AI engagements, particularly where architecture, scale, complexity or delivery risk requires senior leadership.

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Lead across multiple projects and technical workstreams, setting priorities and direction while ensuring teams can execute without becoming dependent on you for every decision.

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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.

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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.

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Run rigorous design reviews that raise the engineering bar across the practice and create an environment where technical decisions are challenged regardless of seniority.

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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.

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Own the technical quality of major AI proposals, translating solution concepts into credible architectures, scopes, delivery models, team structures, estimates and commercial assumptions.

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Work with commercial and account leadership to shape technical propositions, identify opportunities and determine where the AI Engineering practice should invest and differentiate.

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Develop senior engineers and technical leads, building the leadership depth and succession required to scale the department.

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Shape the AI Engineering capability plan, including hiring priorities, skills development, team composition and the bar for senior technical talent.

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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.

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Experience operating beyond individual project leadership, with responsibility for technical direction, engineering quality or capability across multiple teams.

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Deep technical credibility. You’re comfortable working in production Python, substantial codebases and API-driven systems that need to perform reliably at scale.

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Recent, personal experience architecting and building production AI systems. Expect to discuss retrieval strategy, caching, context economics, servi

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