Senior Field-Deploy AI Platform Engineer

McKinsey & Company; Boston (MA); On-site; USD 216,700 - 220,000; Full time

Benefits offered by this job

Medical coverage

Dental and vision coverage

Paid time off

Retirement contributions

Parental leave

Job summary

McKinsey & Company is seeking a Principal Forward Deployed Engineer to lead deployment and scaling of an advanced AI platform across cloud and on‑prem environments. You will own end‑to‑end delivery across multiple workstreams, guide Kubernetes‑based deployments, and work closely with client teams to translate AI strategies into practical, scalable solutions.

You’ll collaborate with data scientists and engineers, mentor team members, and influence delivery practices while operating in dynamic,

Qualifications

• Bachelor’s, 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
• Strong experience with CI/CD pipelines and Infrastructure as Code (e.g., 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
• Strong problem‑solving skills with a structured approach to debugging and resolving issues in complex, non‑standard environments, including operating and troubleshooting distributed systems in production
• Experience with DevSecOps, infrastructure security, and networking fundamentals (e.g., IAM/SSO, RBAC, secrets management, VPNs, DNS, load balancing); experience with delivery standards, runbook development, and automation frameworks is a strong advantage
• Familiarity with observability, monitoring, and compliance practices, and experience working in secure or regulated environments is preferred
• Willingness to travel
• Ability to communicate effectively in client‑facing settings, including leading technical discussions, facilitating workshops, and presenting to senior stakeholders

Responsibilities

• Lead the deployment and scaling of a next‑generation AI platform across cloud and hybrid environments
• Own the platform delivery lifecycle across large, multi‑workstream engagements
• Architect and guide Kubernetes‑based deployments, containerization, and operational stability
• Collaborate with data scientists and engineers to translate AI strategies into production workflows
• Mentor engineers and contribute to delivery tooling and practices
• Partner with clients in North America to ensure successful adoption and value realization

Skills

Full‑stack engineering (Python, React/

Cloud-native deployments

Kubernetes expertise

CI/CD & IaC

Problem solving

Client communication

Education

Bachelor’s or Master’s in CS/ML/Applied Statistics/Engineering

Tools

Kubernetes

Docker

Terraform

GitHub Actions

Ansible

Helm

Job description

McKinsey & Company is seeking a Principal Forward Deployed Engineer to lead deployment and scaling of an advanced AI platform across cloud and on‑prem environments. You will own end‑to‑end delivery across multiple workstreams, guide Kubernetes‑based deployments, and work closely with client teams to translate AI strategies into practical, scalable solutions.

You’ll collaborate with data scientists and engineers, mentor team members, and influence delivery practices while operating in dynamic,