Driving lasting impact and building long‑term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture – doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward. Every day, you’ll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.

Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development.

Global community: The combination of hands‑on engineering, client‑facing delivery, and direct product influence defines your role. You will sit at the intersection of software engineering and infrastructure, bringing advanced AI capabilities into real‑world environments and ensuring they work at scale. You’ll lead large‑scale, multi‑workstream deployments across cloud and hybrid infrastructures in enterprise environments (e.g., AWS, Azure, GCP), guiding architectural decisions and setting direction for complex deployments across engagements.

Your work frequently involves containerized and distributed systems, including Kubernetes‑based environments, where reliability, scalability, and operational stability are critical. From validating performance and resilience to ensuring structured handovers, your work ensures systems are production‑ready and built to last. Working closely with senior technical stakeholders, you’ll help define deployment strategies, navigate complex challenges, and guide adoption through practical,



experience‑led best practices.

You’ll maintain a continuous feedback loop with the product team — surfacing patterns from the field, contributing to issue resolution, and helping ensure that real‑world deployment experience shapes the platform’s evolution. Based in one of our European offices, you’ll work closely with engineers, product teams, and technical experts across the QuantumBlack, AI by McKinsey, integral community. Beyond leading deployments, you’ll collaborate with data scientists, machine learning engineers, designers, and technologists on interdisciplinary initiatives, contributing to a broader ecosystem of AI innovation.

You’ll also play a key role in developing others—mentoring engineers, reviewing approaches, and helping teams raise the bar for quality and delivery. Bachelor’s or Master’s in computer science, machine learning, applied statistics, mathematics, engineering, artificial intelligence, or a related field. ~8+ years of hands‑on experience in software, platform, or infrastructure engineering, with a track record of leading enterprise‑scale platform rollouts. ~ Strong full‑stack engineering – proficiency in Python and modern web frameworks (React, NextJS or equivalent). ~ Experience designing,



deploying, and managing cloud‑based systems (AWS, Azure, or GCP), including containerization (Docker) and orchestration frameworks, with hands‑on experience operating and troubleshooting production systems; deep expertise in Kubernetes cluster architecture, installation, configuration, and lifecycle management at production scale. ~ Experience leading complex deployments, guiding architectural decisions, and driving delivery standards across engagements in multi‑stakeholder environments. ~ GitHub Actions, GitLab CI, Terraform, Ansible, Helm), contributing to scalable delivery automation. ~ Strong understanding of data architectures and platform design, including hands‑on experience with relational databases (e.g., PostgreSQL) and familiarity with graph databases (e.g., Neo4j), alongside data pipelines and system integration patterns. ~ Experience with AI‑native platform concepts, including model integration patterns, agentic architectures (tool calling, prompt orchestration, multi‑agent workflows), and data pipelines that support AI‑driven applications is a plus. ~ Experience with DevSecOps, infrastructure security, and networking fundamentals (e.g., IAM/SSO, RBAC, secrets management, VPNs, DNS, load balancing); Familiarity with observability, monitoring, and compliance practices, and experience working in secure or regulated environments is preferred. ~ Ability to communicate effectively in client‑facing settings, including leading technical discussions, facilitating workshops, and presenting to senior stakeholders. #

📌 Engineering Manager, hibrido (Madrid)
🏢 QuantumBlack, AI by McKinsey
📍 Madrid