Lead Engineer – Data Platforms, Performance & Agentic AI (W2 Role)

Position: Lead Engineer (Contract) – Data Platforms, Performance & Agentic AI Location: Remote (EST) Duration: Multi-Year Contract In this role, you will: Architecture, Data Engineering & Implementation • 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 Agentic AI & AI-Driven Development • 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 • 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 Performance Engineering & Operational Readiness • 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 About You You have: • 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