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

Experience with MCP servers and agents skills