Lead Engineer, Full Stack Platform Engineer

In this role, you will:

• 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

• 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

• 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
• Implement robust observability, telemetry, and performance monitoring across all layers
• Establish and enforce standards for automation, reliability, and performance engineering
• Integrate AI‑driven components (prediction, anomaly detection, intelligent insights) into production systems

• 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

• Act as a senior technical leader driving architectural decisions and solving complex system challenges
• Mentor engineers across backend, data, performance, and AI domains
• Champion engineering best practices in performance optimization, scalability, security, and reliability
• Clearly communicate technical strategy, trade‑offs, and decisions to stakeholders

• Lead performance engineering efforts, including load testing, capacity planning, and system tuning
• Build frameworks for data‑driven performance benchmarking and optimization
• Ensure systems meet strict SLAs for availability, latency, and scalability
• Proactively identify risks and ensure readiness for high‑stakes operational events

• 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 (Type Script 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/Type Script) for data‑intensive interfaces
• Deep knowledge of AWS services (Lambda, S3, Step Functions, SNS/SQS, Redshift, Athena, Dynamo
  
  DB, etc.)
• Experience with Infrastructure as Code (CDK, Terraform, Cloud Formation)
• Strong understanding of event‑driven architectures, streaming, and telemetry systems
• Experience implementing observability and monitoring solutions (e.g., Grafana or similar)
• Experience…