Senior Forward Deployed Engineer
Posted
Summary
Deploys and scales McKinsey's EcliptOS platform at client sites across cloud and hybrid infrastructure: production-grade Kubernetes/Docker deployments, CI/CD and IaC automation on AWS/Azure/GCP, troubleshooting, and enabling clients to run AI-driven workflows. Core stack: Python, React/Next.js, PostgreSQL, Neo4j.
- Deployment and scaling of EcliptOS platform
- Ownership of EcliptOS platform delivery lifecycle
- Deployment and configuration of production-grade systems across cloud and hybrid infrastructures
- Validation of performance and stability of deployed systems
- Ensuring structured handovers for long-term success
- Troubleshooting end-to-end issues in containerized and distributed systems
- Contribution to deployment tooling and automation
- Providing product feedback to improve platform reliability and delivery
- Enabling clients to run AI-driven workflows within existing technology ecosystems
- Ensuring environment readiness and resolving deployment challenges
- Guiding adoption through practical best practices
- Collaborating with engineers, product teams, and technical experts
- Contributing to interdisciplinary initiatives with data scientists, machine learning engineers, designers, and technologists
- Documenting patterns and refining delivery practices
- Supporting colleagues across engagements
- 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 enterprise platform deployments
- Strong engineering fundamentals with proficiency in Python
- Familiarity with modern full-stack development such as React or NextJS
- Experience designing, deploying, and managing cloud-based systems including AWS, Azure, or GCP
- Hands-on experience with containerization using Docker and orchestration frameworks
- 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 such as GitHub Actions, GitLab CI, Terraform, Ansible, Helm
- Strong understanding of data architectures and platform design including relational and graph databases like PostgreSQL and Neo4j
- Strong problem-solving skills with structured debugging and issue resolution in complex environments
- Willingness to travel
- Ability to communicate effectively with technical and non-technical stakeholders in client-facing settings including delivering walkthroughs, documentation, and training
- Familiarity with AI platform concepts including model integration patterns, agentic workflows, and AI-driven application interactions with data and infrastructure
- Experience with DevSecOps and infrastructure security practices such as IAM/SSO, RBAC, secrets management, encryption
- Experience with deployment standards, runbook development, and automation frameworks
- Familiarity with observability, monitoring, and compliance tooling such as Prometheus, Grafana, ELK
- Experience working in secure or regulated environments
- Continuous learning culture with structured programs and clear, actionable feedback
- Opportunity to have a voice that matters with tangible impact and innovative ideas
- Global community with colleagues across 65+ countries and over 100 nationalities
- Competitive salary based on location, experience, and skills
- Comprehensive benefits package for holistic well-being
Skills
- Agentic AI
- AI
- Ansible
- Automation
- AWS
- Azure
- CI/CD
- Cloud
- Containerization
- DevSecOps
- Distributed Systems
- Docker
- ELK
- GCP
- GitHub
- GitHub Actions
- GitLab
- Grafana
- Helm
- IAM
- Infrastructure as Code
- Kubernetes
- Machine Learning
- Neo4j
- Next.js
- Observability
- PostgreSQL
- Prometheus
- Python
- RBAC
- React
- Secrets Management
- SSO
- Statistics
- Terraform