Live opening · Posted 5 days ago

Lead Engineer – Full Stack Platform Engineer

GlobalPoint · United States (Remote)
Linkedin Yes
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At a glance

The key details from the original listing.

Posted 5 days ago
CompanyGlobalPoint
LocationUnited States (Remote)
Salary$85/hr - $90/hr
Work modeYes
SourceLinkedin
Listed5 days ago

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About the role

Description supplied by the original job listing.

Lead Engineer Full Stack Platform Engineer
Location: Remote EST Time Zone
Contract: Long-Term Contract
Position Overview
We are seeking a Lead Full Stack Platform Engineer to own the technical architecture and end-to-end delivery of high-performance, cloud-native platforms.
The ideal candidate will bring strong experience across Full Stack Engineering, AWS, Data Platforms, Performance Engineering, and Agentic AI. This is a hands-on technical leadership role requiring excellent communication and the ability to drive complex engineering initiatives from architecture through implementation.
Key Responsibilities
Own end-to-end engineering solutions spanning data generation, ingestion, APIs, analytics, and user-facing applications.
Lead the architecture and development of scalable, high-performance AWS cloud platforms.
Design data generation and event-driven systems supporting testing, analytics, and AI development.
Build and optimize systems for performance, scalability, reliability, latency, and cost efficiency.
Develop backend services and APIs using Node.js/TypeScript and Python.
Build modern front-end applications using React and TypeScript.
Design and manage real-time and batch data pipelines and data platforms.
Implement observability, telemetry, monitoring, and performance engineering solutions.
Lead technical design discussions, code reviews, sprint planning, and architectural decisions.
Mentor engineers across backend, data, performance, and AI domains.
Develop and integrate Agentic AI solutions into engineering and operational workflows.
Work with LLMs and AI orchestration frameworks to automate data, testing, and engineering processes.
Evaluate AI-native architectures including tool-using agents and multi-agent systems.
Apply AI-assisted development tools to improve developer productivity.
Ensure AI solutions are secure, scalable, reliable, and production-ready.
Required Skills
7+ years of experience building and operating scalable, distributed, cloud-native systems.
Strong end-to-end system architecture and design experience.
Strong performance engineering experience, including profiling, load testing, capacity planning, and optimization.
Hands-on experience with Node.js / TypeScript and Python.
Strong experience building APIs and event-driven applications.
Experience designing and operating real-time and batch data pipelines.
Strong experience with React / TypeScript.
Deep AWS experience with services such as:
AWS Lambda
S3
Step Functions
SNS/SQS
Redshift
Athena
DynamoDB
Experience with Infrastructure as Code using AWS CDK, Terraform, or CloudFormation.
Strong understanding of event-driven architecture, streaming, and telemetry.
Experience with observability/monitoring tools such as Grafana or similar.
Experience integrating and operationalizing AI/ML systems in production.
Agentic AI Requirements
Candidates should have hands-on exposure to modern AI engineering, including:
LLMs and AI orchestration frameworks
Agentic workflows and autonomous systems
AI-assisted coding tools such as GitHub Copilot, ChatGPT, or similar
RAG architectures
Prompt engineering
Tool-augmented AI systems
Multi-agent architectures and intelligent automation
MCP servers and agent skills are a plus.
Preferred / Nice-to-Have Skills
Experience in high-scale, mission-critical environments.
Cell-based or multi-tenant architecture experience.
Experience with data isolation, security, and performance segmentation.
Synthetic data generation or simulation experience.
Multi-agent AI systems.
Advanced automation pipelines.
MCP servers and agent-based systems.
Core Technology Stack
AWS: Lambda, S3, DynamoDB, SNS/SQS, EventBridge, Kinesis, Step Functions, Redshift, Athena
Backend: Node.js, TypeScript, Python
Frontend: React, TypeScript
Search: OpenSearch / Elasticsearch
Infrastructure: AWS CDK, Terraform, CloudFormation
AI: LLMs, Agentic AI, RAG, AI Agents, AI-assisted development
Observability: Grafana / Monitoring / Telemetry
Architecture: Microservices, Event-Driven Architecture, Distributed Systems

Work arrangement
Yes

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