Senior Forward Deployed Engineer - QuantumBlack, AI by McKinsey

Your Impact

You’ll deploy and scale a next-generation AI platform designed to connect strategy to execution through advanced analytics, machine learning, and agentic systems. 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 play a key role in how these systems are deployed, adopted, and operated, enabling organizations to translate complex AI strategies into practical, high-impact solutions.

Responsibilities

• Deploy and configure production-grade systems across cloud and hybrid infrastructures (e.g., AWS, Azure, GCP)
• Validate performance and stability
• Ensure structured handovers for long-term success
• Work often involves containerized and distributed systems, including Kubernetes-based environments
• Reliability and operational stability are critical
• Own the platform delivery lifecycle, bridging product engineering and client environments
• Troubleshoot issues end-to-end
• Ensure platforms are fully production-ready
• Contribute to deployment tooling, automation, and product feedback loops to improve platform reliability and delivery

Qualifications and Skills

• Bachelor’s or Master’s degree in computer science, machine learning, applied statistics, mathematics, engineering, artificial intelligence, or a related field
• 4+ years of hands-on experience as a Software or Platform Engineer, with exposure to enterprise platform deployments
• Strong engineering fundamentals with proficiency in Python and familiarity with modern full-stack development (e.g., 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 with CI/CD pipelines and Infrastructure as Code (e.g., GitHub Actions, GitLab CI, Terraform, Ansible, Helm)
• Strong understanding of data architectures and platform design, including relational and graph databases (e.g., PostgreSQL, Neo4j), data pipelines, and system integration patterns across structured and unstructured data
• Familiarity with AI platform concepts, including model integration patterns, agentic workflows, and how AI-driven applications interact with underlying data and infrastructure is a plus
• Strong problem-solving skills with a structured approach to debugging and resolving issues in complex, non-standard environments
• Experience with DevSecOps and infrastructure security practices (e.g., IAM/SSO, RBAC, secrets management, encryption)
• Experience with deployment standards, runbook development, and automation frameworks is a strong advantage
• Familiarity with observability, monitoring, and compliance tooling (e.g., Prometheus, Grafana, ELK) and experience working in secure or regulated environments is a plus
• Willingness to travel
• Ability to communicate effectively with technical and non-technical stakeholders in client-facing settings, including delivering walkthroughs, documentation, and training