Lead Engineer- Data Platforms, Performance & Agentic AI

Ideal Candidate Profile: Seeking a Lead Engineer- Data Platforms, Performance & Agentic AI that owns the technical architecture - full stack, strong data and app performance experience, Agentic AI with solid communication skills. Candidate should be skilled in designing and deploying agentic AI systems using LLMs and AI-assisted development tools. A strong technical leader with excellent communication skills, driving architecture, scalability, and engineering excellence and hands-on experience with Node.js, Python, React, and AWS, with proven experience in real-time data pipelines and event-driven architectures Job Duties & Responsibilities End-to-End Solution Ownership & Product Engineering (40%) 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 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 (20%) 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 Required Skills & Experience 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 Preferred Skills 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