Lead Full Stack Engineer - Strong AI/Data expertise, React etc

Job Description

Job DescriptionThe RoleArchitect, build, and maintain full-stack web applications using React, Next.js, Node.js, MongoDB, and modern cloud-native tools.
Build pixel-perfect, high-performance front-ends with deep attention to detail and strong design intuition.
Design and implement scalable data pipelines and infrastructure for real-time and batch processing.
Create applications with deep integration of analytics, reporting, and data-driven decision-making tools.
Integrate and operationalize AI APIs such as OpenAI, Anthropic, or other LLM providers into customer-facing features.
Own the full development lifecycle — from architecture and database design to deployment, observability, and scaling.
Collaborate closely with cross-functional teams to rapidly iterate on AI-powered product features.
Mentor junior engineers and contribute to a high-ownership, high-performance culture.The candidate needs to be truly full stack (must be able to build an application from scratch - all the way through deploying it on AWS - all by themselves)NOT looking for AI or Data engineers, but full stack engineers with deep experience in at least one of these two areas: AI or Data, (ex. not necessarily building AI models from scratch but building features on top of AI infrastructure or using LLMs to develop AI features)MUST have an engineering degree - non engineering degrees or bootcamps is a nonstarterSkills7+ years of full-stack engineering experience, with a proven ability to build and launch full web applications from scratch.
Mastery of React, Next.js, Node.js, and MongoDB.
Proven experience integrating and scaling AI APIs in production environments, including building complex, production-level microservices with an orchestration layer.
Experience deploying and scaling AI-powered features in production, with expertise in:
AI Red Teaming - Adversarial testing to find failure modes and vulnerabilities
AI Assurance - Comprehensive validation that AI systems meet safety and reliability standards
Model Monitoring - Continuous oversight of AI model performance and behavior in production
AI Quality Assurance (AI QA) - Traditional QA practices adapted for AI systems
AI Reliability Engineering - Ensuring AI systems are dependable and fail gracefully
Strong expertise in designing and maintaining data pipelines (ETL/ELT, streaming, or batch).
Deep mastery of AWS services, including but not limited to:
Lambda, EC2, S3, RDS, API Gateway, SQS, SNS, CloudWatch, IAM, VPC, and ECS with Containerization
DevOps experience including but not limited to:
Docker, Swarm, Github Actions, Creating Custom CI/CD pipelines. Knowledge of Kubernetes is a plus.
Demonstrated ability to scale applications to millions of users.
Strong understanding of system architecture, security, caching strategies, and performance optimization.
Demonstrated ability to scale applications to millions of users.Bonus: You’ve founded a startup or been the first/early technical hire at an early-stage startup.