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Job details:
BASIC PURPOSE:
The position of Senior AI Full Stack Developer reports to the Team Lead or Project Manager. The Senior AI Full Stack Developer is a hands-on individual contributor responsible for analyzing business requirements, designing software solutions, developing and deploying applications, performing code reviews, and supporting application delivery.
The role requires strong expertise in modern full-stack engineering using TypeScript, React, Next.js, Node.js, AWS, and Aurora PostgreSQL while leveraging Artificial Intelligence and Large Language Model (LLM) technologies to build scalable, secure, and intelligent enterprise applications.
The ideal candidate combines strong software engineering fundamentals with practical experience building and deploying AI-powered applications in production environments. The role owns end-to-end delivery of AI-enabled features it builds, from data grounding and retrieval design through agent design, application integration, deployment, production monitoring, optimization, and ongoing support. As the accountable senior engineer, this individual is responsible for delivering AI-enabled business solutions that seamlessly integrate with enterprise applications while maintaining high standards of performance, scalability, reliability, security, and user experience.
ESSENTIAL FUNCTIONS:
- 5+ years working as a software engineer building production applications, including hands-on delivery of AI or LLM-backed features on AWS.
- Proven experience delivering enterprise-grade full-stack applications using TypeScript, Next.js, Node.js, AWS, and Aurora PostgreSQL.
- Proven experience delivering AI-enabled product features integrated into production business applications.
- Proven full-stack delivery of enterprise-grade pipelines and web applications, including event-driven architecture and distributed systems on AWS.
- Strong understanding of Generative AI and Agentic AI concepts, including prompting, Retrieval-Augmented Generation (RAG), embeddings, vector search, agents, tool usage, and AI evaluation methodologies.
- Extensive experience shipping AI-enabled solutions built on Amazon Bedrock, Bedrock AgentCore, AWS Glue, and AWS Lambda integrated into enterprise applications rather than standalone prototypes or notebooks.
- Experience designing and implementing scalable data pipelines supporting AI workloads and intelligent application features.
- Strong understanding of AI operational practices including evaluation, prompt lifecycle management, observability, monitoring, governance, model management, cost optimization, and continuous improvement.
- Experience designing AI guardrails, governance controls, evaluation frameworks, model monitoring, and production AI observability.
- Experience securing AI applications against prompt injection, sensitive information disclosure, data leakage, and other common LLM security risks.
- Production experience with Python for AI workloads, backend services, automation, and data pipelines, and TypeScript/Node.js for enterprise application development.
- Strong understanding of software security practices including authentication, authorization, encryption in transit and at rest, audit logging, secrets management, dependency management, and secure application development.
- Experience designing and implementing event-driven architectures using AWS services including EventBridge, SQS, SNS, Step Functions, and enterprise integration patterns.
- Experience with containerization technologies such as Docker and deployment of containerized applications within AWS environments.
- Strong knowledge of software engineering best practices including version control, testing, code reviews, architecture patterns, Infrastructure as Code, CI/CD, and Agile development methodologies.
- Familiarity with Git-based source control and collaborative development practices.
- Ability to understand business requirements and translate them into scalable technical solutions and implementation plans.
- Excellent analytical, problem-solving, communication, and stakeholder management skills.
- Self-motivated and capable of working independently, taking ownership of solutions from concept through production support while effectively managing priorities in a fast-paced environment.
QUALIFICATIONS:
- 5-7+ years working as a software engineer building production applications, including hands-on delivery of AI or LLM-backed features on AWS.
- Proven experience delivering enterprise-grade full-stack applications using TypeScript, Next.js, Node.js, AWS, and Aurora PostgreSQL.
- Proven experience delivering AI-enabled product features integrated into production business applications.
- Proven full-stack delivery of enterprise-grade pipelines and web applications, including event-driven architecture and distributed systems on AWS.
- Strong understanding of Generative AI and Agentic AI concepts, including prompting, Retrieval-Augmented Generation (RAG), embeddings, vector search, agents, tool usage, and AI evaluation methodologies.
- Extensive experience shipping AI-enabled solutions built on Amazon Bedrock, Bedrock AgentCore, AWS Glue, and AWS Lambda integrated into enterprise applications rather than standalone prototypes or notebooks.
- Experience designing and implementing scalable data pipelines supporting AI workloads and intelligent application features.
- Strong understanding of AI operational practices including evaluation, prompt lifecycle management, observability, monitoring, governance, model management, cost optimization, and continuous improvement.
- Experience designing AI guardrails, governance controls, evaluation frameworks, model monitoring, and production AI observability.
- Experience securing AI applications against prompt injection, sensitive information disclosure, data leakage, and other common LLM security risks.
- Production experience with Python for AI workloads, backend services, automation, and data pipelines, and TypeScript/Node.js for enterprise application development.
- Strong understanding of software security practices including authentication, authorization, encryption in transit and at rest, audit logging, secrets management, dependency management, and secure application development.
- Experience designing and implementing event-driven architectures using AWS services including EventBridge, SQS, SNS, Step Functions, and enterprise integration patterns.
- Experience with containerization technologies such as Docker and deployment of containerized applications within AWS environments.
- Strong knowledge of software engineering best practices including version control, testing, code reviews, architecture patterns, Infrastructure as Code, CI/CD, and Agile development methodologies.
- Familiarity with Git-based source control and collaborative development practices.
- Ability to understand business requirements and translate them into scalable technical solutions and implementation plans.
- Excellent analytical, problem-solving, communication, and stakeholder management skills.
- Self-motivated and capable of working independently, taking ownership of solutions from concept through production support while effectively managing priorities in a fast-paced environment.
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