Associate Director, AI Engineering

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Blend360

WebsiteLinkedInAll Job Openings

501 - 1000 employees

🏥 Healthcare

🏨 Hospitality

✈️ Travel

💰 $100M Private Equity Round on 2022-08

Healthcare • Hospitality • Travel

Blend360 is a professional services company specializing in AI, data analytics, and data-driven solutions. They work with Fortune 1000 and large enterprise brands to tackle significant challenges by integrating people and artificial intelligence. Blend360 focuses on several domains including business intelligence, data engineering, data science, MLOps, and data governance. Their industries of expertise encompass financial services, energy, healthcare and life sciences, retail, technology, media & telecom, and travel & hospitality. Blend360 is recognized for their AI and data solutions, having earned accolades such as "AI-Enabling Solution of the Year" and being listed among the "Top Generative AI Service Providers 2024.

📋 Description

• Set technical direction across AI Engineering, defining architecture principles, engineering standards, delivery practices and technical capabilities • Own the technical quality of major AI engagements, especially those involving architecture, scale, complexity or delivery risk • Lead multiple projects and technical workstreams, setting priorities and direction while enabling team execution • Design and review production AI architectures, including retrieval and knowledge layers, agentic pipelines, evaluation and multilingual systems • Evaluate cost, latency, reliability and scalability trade-offs • Review code, prototype approaches, resolve architectural issues and work directly with engineers on difficult problems • Run rigorous design reviews and raise engineering standards across the practice • Act as senior technical counterpart to clients, including CxO and architecture leadership • Own the technical quality of major AI proposals, including architectures, scopes, delivery models, team structures, estimates and commercial assumptions • Shape technical propositions, identify opportunities and guide AI Engineering practice investments • Develop senior engineers and technical leads and build leadership depth and succession • Shape the AI Engineering capability plan, including hiring priorities, skills development, team composition and senior technical talent standards

🎯 Requirements

• At least 10 years’ experience across AI, data and software engineering • 3+ years leading engineering teams or a substantial technical function within consulting or professional services • Experience beyond individual project leadership, with responsibility for technical direction, engineering quality or capability across multiple teams • Production Python experience with substantial codebases and API-driven systems operating reliably at scale • Recent personal experience architecting and building production AI systems • Ability to discuss retrieval strategy, caching, context economics, serving constraints, evaluation, observability and practical failures • Strong systems thinking and ability to reason through unfamiliar platforms and problems • Experience leading complex programmes or multiple concurrent engineering workstreams with accountability for technical direction, planning, resourcing, risk and delivery outcomes • Judgement to intervene personally when needed and delegate effectively while retaining accountability for technical quality • Experience developing senior engineers and technical leaders and raising engineering standards across an organisation • Commercial awareness to turn technical solutions into realistic scopes, team structures, estimates and delivery plans • Experience contributing to account growth, technical propositions or go-to-market activity within a consulting organisation • Confidence working with senior clients and executives while remaining credible with engineers at code and architecture level • Ability to make difficult technical decisions, create clarity amid ambiguity and take responsibility for outcomes • Strong experience with Databricks and Azure OpenAI • Nice to have: ontology, knowledge graph or semantic layer experience • Nice to have: delivery experience in pharma or CPG • Nice to have: practical experience designing systems around EU AI Act requirements • Nice to have: multilingual AI systems in production • Nice to have: presence in the Databricks or Microsoft partner ecosystem • Nice to have: experience shaping go-to-market and commercial strategy for an AI Engineering practice