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
Experience with MCP servers and agents skills
