Lead Engineer, Full Stack Platform Engineer

Description: Candidate offered backed out for FTE (screen for this) -do not resubmit candidates that were reviewed and not selected to interview (check activity) - ASK if there are questions before submitting. Need fresh options, thank you. Lead Engineer (Contract) - Data Platforms, Performance & Agentic AI, Profile: Lead Engineer that owns the technical architecture - full stack, strong data and app performance experience, Agentic AI solid communication skills Rates: standard Process: submit tech stack grid back with info as “why a fit for this role vs career overview” > initial .5 video w/manager followed by a 1-1.5 tech panel > remote start on a Tuesday (8:45-9:00am EST orientation)

About the Team The team owns the platform end-to-end-from data generation and ingestion through application delivery and user experience. We are responsible for building and maintaining high-performance, resilient systems, with a strong emphasis on performance engineering, scalability, and operational readiness. A key part of our mission is enabling the organization through robust test data generation and simulation capabilities that support platform validation, reliability testing, and AI model development.

Technology NO Awareness of technology (0) LITTLE Awareness - read/heard of technology (1) EXPOSURE to technology in environment (2) SOME development in technology (3) Very COMFORTABLE developing in technology (4) EXPERTISE in technology i.e. could teach a class (5) AWS - Dynamo X AWS - Lambda X AWS - S3 X AWS - SNS/SQS/ Event bridge/ Kinesis X JavaScript (ES6) X OpenSearch/ Elastic Search X node.js Development *distinguish coding or NPM X React.js X, Own delivery of complex, end-to-end engineering solutions-from data generation and ingestion through analytics, APIs, and user-facing experiences

Develop a deep understanding of business workflows, especially high-scale exam and operational systems

Partner with product, architecture, and engineering teams to shape requirements, define scope, and provide accurate level-of-effort estimates

Drive sprint planning, technical design discussions, and code/design reviews with a focus on speed, quality, and scalability

Architecture, Data Engineering & Implementation (40%)

Lead design and implementation of scalable, high-performance, cloud-native data and application platforms

Architect data generation systems (synthetic, event-based, telemetry-driven) to support testing, analytics, and AI model development

Engineer high-performance systems, focusing on latency, throughput, resiliency, and cost efficiency, Design and build agentic AI systems that can autonomously reason, plan, and execute tasks across engineering workflows

Leverage LLMs and orchestration frameworks to enable intelligent automation in data pipelines, testing, and operations

Incorporate AI-assisted development practices, including code generation, code review augmentation, and developer productivity tooling

Evaluate and implement AI-native architectures, including tool-using agents, multi-agent systems

Ensure responsible, secure, and scalable deployment of AI capabilities in production environments

Technical Leadership & Engineering Excellence

Act as a senior technical leader driving architectural decisions and solving complex system challenges

7+ years of experience building and operating scalable, distributed, cloud-native systems, including data platforms and APIs

Strong experience with end-to-end system design, from data generation to front-end delivery

Proven expertise in performance engineering, including profiling, load testing, and system optimization

Hands-on experience with backend technologies such as Node.js (TypeScript preferred) and Python, building APIs and event-driven systems

Strong experience designing and operating data pipelines and data platforms (real-time and batch)

Experience building modern front-end applications (React/TypeScript) for data-intensive interfaces

Deep knowledge of AWS services (Lambda, S3, Step Functions, SNS/SQS, Redshift, Athena, DynamoDB, etc.)

Experience with Infrastructure as Code (CDK, Terraform, CloudFormation)

Strong understanding of event-driven architectures, streaming, and telemetry systems

Experience implementing observability and monitoring solutions (e.g., Grafana or similar)

Experience with AI/ML systems in production, including model integration and operationalization

AI & Modern Engineering Capabilities

Experience working with LLMs, agent frameworks, or AI orchestration tools

Familiarity with agentic workflows, autonomous system

Hands-on experience with AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, or similar) and integrating them into development workflows

Understanding of RAG architectures, prompt engineering, and tool-augmented AI systems

Nice to Have

Experience in high-scale, mission-critical environments with strict reliability requirements

Familiarity with cell-based or multi-tenant architectures

Experience designing systems for data isolation, security, and performance segmentation

Exposure to synthetic data generation or simulation systems

Experience with multi-agent AI systems or advanced automation pipelines

Experience with MCP servers and agents skills

What We’re Looking For

Strong ownership mindset with the ability to drive end-to-end delivery

Deep focus on performance, scalability, and reliability

Curiosity and hands-on engagement with emerging AI technologies

Ability to operate effectively in a fast-moving, contract-based environment

Clear communication and strong collaboration across technical and non-technical stakeholders

Skills: Agentic AI, AWS Dynamo, AWS Elastic Search, AWS Event Bridge, AWS Kinesis, AWS Lambda, AWS Open Search, AWS S3, AWS SNS/SQS, JavaScript ES6, node