What are the responsibilities and job description for the AI Fullstack Engineer position at mroads?
Mroads is looking for an "AI Fullstack Engineer" for one of the direct clients. This is a remote opportunity with travel once a quarter to Bethesda, MD.
Job Responsibilities
- Develop and maintain scalable full-stack applications using JavaScript technologies with expertise in React, Node.js, GraphQL, and NX.
- Design, develop, and integrate AI-powered features leveraging Large Language Models (LLMs), Langchain, Generative AI, and intelligent automation capabilities.
- Build and consume APIs for AI/LLM services, including prompt orchestration, model integration, and retrieval-based solutions.
- Collaborate with product, engineering, and AI teams to identify and implement AI-driven user experiences and business workflows.
- Implement DevOps best practices and contribute to continuous improvement of the software development lifecycle.
- Utilize Git for version control and collaborative development across distributed teams.
- Implement and manage CI/CD pipelines to ensure reliable, secure, and efficient code delivery.
- Deploy, monitor, and optimize cloud-native applications and AI services in Kubernetes environments.
- Architect innovative, scalable, and secure solutions while contributing to platform engineering initiatives.
- Evaluate emerging AI technologies, frameworks, and tools to drive innovation and improve development efficiency.
- Ensure application quality through automated testing, code reviews, observability, and performance optimization.
- Demonstrate a passion for delivering high-quality, customer-focused solutions.
Requirements
- Proven experience as a Full Stack JavaScript Developer with expertise in React, Node.js, GraphQL, and NX.
- Hands-on experience integrating AI/ML services, Large Language Models (LLMs), and Generative AI capabilities into enterprise applications.
- Experience working with AI platforms and frameworks such as OpenAI APIs, Anthropic, Gemini, LangChain, LlamaIndex, Vector Databases, RAG (Retrieval-Augmented Generation), or similar technologies.
- Strong understanding of prompt engineering, AI application design patterns, and responsible AI practices.
- Experience building and consuming RESTful APIs, GraphQL APIs, and AI service integrations.
- Proficiency in Kubernetes and containerized application deployment.
- Experience with CI/CD pipelines, DevOps practices, and cloud platforms such as AWS, Azure, or Google Cloud.
- Strong architectural skills with the ability to design scalable, resilient, and secure solutions.
- Knowledge of observability, monitoring, logging, and performance tuning in distributed systems.
- Excellent problem-solving, communication, and collaboration skills.
- Passionate about software quality, innovation, and leveraging AI technologies to solve business challenges.