Lead Engineer, Full Stack Platform Engineer

Dice is seeking a Lead Engineer to own the technical architecture and deliver complex engineering solutions. The role involves end-to-end solution ownership, architecture, data engineering, and implementing AI-driven development practices.

Responsibilities:

• 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, tradeoffs, 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

Requirements:

• 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
• 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