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.