Full Stack Engineer (AI)

BAMM MED STAFFING LLC

Newark, NJ, United States

10 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.disabledperson.com

Tech stack

Clean Code Principles Java (Programming Language) Application Programming Interfaces (APIs) Agile Methodology Artificial Intelligence Amazon Web Services Application Integration Architecture Software Applications Automation of Tests Cloud Computing Cloud Engineering Software Quality

+30 more

Code Review Databases Continuous Delivery Continuous Integration Software Design Patterns Elasticsearch Python (Programming Language) Machine Learning MongoDB Oracle (Applications) Scrum Methodology Queueing Systems Secure Coding Software Engineering SQL Databases Strategies of Testing Toolchain Chatbots ReactJS Retrieval-Augmented Generation AI Coding Agents Backend Agentic-AI Event Driven Architecture Non-relational Database Graphql Front End Software Development Restful APIs Code Restructuring Automation Anywhere

Job description

Lead the design, development, and delivery of full stack AI-enabled applications and Conversational AI.
Provide technical leadership across frontend, backend, API, database, and AI integration workstreams.
Architect scalable, maintainable, and secure solutions using React, Python, Java, REST APIs, GraphQL, and database technologies.
Guide the implementation of AI agents, AI workflows, RAG and intelligent automation capabilities.
Establish and promote engineering best practices, including clean code, reusable components, automated testing, CI/CD, and design patterns.
Drive integration with relational and non-relational databases, including SQL, Oracle, ElasticSearch and MongoDB.
Collaborate with Agile SCRUM teams through sprint planning, backlog refinement, daily standups, demos, and retrospectives.
Stay current with emerging AI, GenAI, full stack, and cloud-native engineering trends, and assess practical adoption opportunities.
Experience with message queues, streaming platforms, pub/sub models, or event-driven architectures.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Requirements

Minimum of 5+ years of full stack application development experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in REACT and one or more programming language(s)
Proficiency in automation and continuous delivery methods
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Practical cloud native experience ex. AWS
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

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