Senior Forward Deployed Engineer - QuantumBlack, AI by McKinsey

About the role

You’ll deploy and scale a next-generation AI platform designed to connect strategy to execution through advanced analytics, machine learning, and agentic systems.

Responsibilities

• Deploy and configure production-grade systems across cloud and hybrid infrastructures (e.g., AWS, Azure, GCP)
• Validate performance and stability of deployed systems
• Ensure structured handovers for long-term success
• Work with containerized and distributed systems, including Kubernetes-based environments
• Troubleshoot issues end-to-end and ensure platforms are fully production-ready
• Contribute to deployment tooling, automation, and product feedback loops to improve platform reliability and delivery

Requirements

• 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) is a plus
• 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

Qualifications

• Hands-on experience with cloud platforms (AWS, Azure, GCP)
• Experience with Kubernetes and containerization
• Proficiency in Python and modern full-stack development tools
• Understanding of data architectures and platform design
• Experience with CI/CD pipelines and Infrastructure as Code
• Strong problem-solving and debugging skills
• Experience with DevSecOps and infrastructure security
• Experience with observability and monitoring tools
• Effective communication skills