AI \ ML Engineer R&D

Cengage Group

1d•Remote

About The Position

The AI/ML Engineer for R&D is a high-output, full-stack engineering role aimed at quickly producing fully functional AI-powered application prototypes. This is a hands-on position for a versatile engineer who acts swiftly, codes in multiple languages, and uses AI-assisted development tools to considerably accelerate delivery. Your role involves turning concepts into functional applications, not simply mock-ups or wireframes. You will build prototypes that prove business value and technical feasibility. The ideal applicant is proficient in modern programming languages and has strong experience working with AWS and Azure clouds. You adopt AI-forward development techniques using tools like GitHub Copilot, Claude, and other AI coding assistants. You have engineering-level knowledge of LLMs and core AI/ML concepts and can integrate these technologies into production-ready software.

Requirements

Bachelor's degree in computer science, Software Engineering, or related field; or equivalent experience
5+ years of professional software engineering experience building production applications
Solid experience in Python and JavaScript/TypeScript; familiarity with at least one other programming language
Extensive practical experience working with both AWS and Azure cloud platforms
Demonstrated experience building AI/ML-powered applications with LLMs and modern AI frameworks
Familiarity with LLM internals, prompt engineering, RAG architectures, and AI/ML principles
Regular user of AI coding assistants with proven methods for AI-enhanced development
Experience with containerization, serverless architectures, and infrastructure-as-code
Strong problem-solving skills; ability to work independently and ship fast with minimal direction

Nice To Haves

Experience with fine-tuning LLMs and custom model training
Experience in developing agentic AI systems and autonomous workflows
AWS and/or Azure certifications (Solutions Architect, Developer, AI/ML Specialty)
Experience with real-time applications, WebSockets, and streaming architectures
Contributions to open-source AI/ML projects
Experience in EdTech or learning technology industry
Familiarity with MLOps practices and model deployment pipelines
Full-stack development experience including modern frontend frameworks

Responsibilities

Rapid Prototype Development Produce fully working AI-enhanced application prototypes swiftly—from initial concept to working demonstration in just days
Develop end-to-end solutions including backend services, APIs, data pipelines, and frontend interfaces
Transform business requirements and technical concepts into tangible, demonstrable applications
Iterate rapidly based on feedback, adjusting quickly to refine or redirect prototypes
Create reusable code libraries, templates, and scaffolding to accelerate future development
Ensure prototypes are sufficiently robust to support collaborator demos and user testing
Leverage AI coding assistants (GitHub Copilot, Claude, Cursor, Codex) to improve development speed and quality
Keep up to date with new AI development tools and incorporate them into everyday workflows
Use LLMs for code generation, debugging, refactoring, documentation, and test creation
Develop prompt engineering techniques to optimize AI-assisted coding output
Contribute to team guidelines for AI-augmented software development
Assess and suggest new AI development tools and methodologies
Develop and launch applications on AWS and Azure cloud platforms with deep fluency in both
Leverage managed AI/ML services including AWS Bedrock, SageMaker, Azure OpenAI, and Azure ML
Implement serverless architectures (Lambda, Azure Functions) for rapid, scalable deployments
Design and build containerized applications using Docker and Kubernetes
Configure cloud infrastructure using IaC tools (Terraform, CloudFormation, Bicep)
Optimize for cost, performance, and security in cloud environments
Integrate LLMs and foundation models into applications with deep understanding of their capabilities and limitations
Implement RAG (Retrieval-Augmented Generation) architectures, vector databases, and embedding pipelines
Build prompt engineering solutions, fine-tuning workflows, and model orchestration patterns
Build agentic AI systems and multi-step reasoning workflows
Apply core ML principles including model evaluation, inference optimization, and responsible AI practices
Connect AI capabilities to enterprise data sources, APIs, and existing systems