Job description

Revature is rapidly growing – both domestically and internationally – and our Team plays a key role in our Corporate Department. We’re looking for an agile and ambitious candidate who is effective in the qualities listed below, all within a rapidly growing environment. Our ideal candidate is based near one of our central offices located in this job posting. Job Description: Experience Required : Minimum 4-5 years of professional experience in AI/ML, Data Science, or Applied Machine Learning. Position Summary: We are seeking a passionate and technically strong Senior Trainer - Artificial Intelligence & Machine Learning to deliver our advanced AI curriculum focused on LLMs, Retrieval-Augmented Generation (RAG), Agentic AI, and end-to-end deployment. The ideal candidate will have a deep understanding of modern AI architectures and the ability to mentor learners in building autonomous, production-grade AI systems - integrating retrieval pipelines, intelligent agents, and deployment workflows across real-world scenarios. Key Responsibilities Deliver engaging, project-based sessions on advanced topics in AI, LLMs, and agentic AI development . Train and mentor learners on: Core AI/ML concepts: supervised & unsupervised learning, deep learning, and NLP. Large Language Models (LLMs): transformer architecture, fine-tuning, and prompt optimization Retrieval-Augmented Generation (RAG): vector databases, document retrieval, embeddings, and knowledge-grounded responses. Agentic AI Systems: Designing and orchestrating AI agents capable of autonomous decision-making Using LangGraph , CrewAI , or AutoGen for multi-agent frameworks Integrating external tools, APIs, and reasoning loops for dynamic task execution Understanding memory management , context persistence , and tool use in agent frameworks AI Deployment & MLOps: Building scalable APIs with FastAPI or Flask Model packaging and orchestration with Docker , Kubernetes , and CI/CD pipelines Model tracking, experimentation, and monitoring with MLflow , Weights & Biases , or Vertex AI Pipelines. Cloud AI Integration: deploying and managing systems on AWS (SageMaker) , Azure ML , or GCP Vertex AI. Lead hands-on projects where learners build RAG-based chatbots , autonomous AI assistants , and deployed LLM applications . Collaborate on curriculum development to integrate cutting-edge AI research and tools into the training modules. Mentor learners through technical challenges, performance optimization, and model deployment. Keep up to date with LLM , agentic AI , and generative AI innovations to ensure curriculum relevance. Required Skills & Qualifications Experience: 4 to 5 years in AI/ML engineering , Data Science , Applied NLP , or MLOps roles. Technical Expertise: Proficiency in Python and AI libraries such as PyTorch , TensorFlow , and Transformers (Hugging Face) . Strong experience with LLMs , prompt engineering , and fine-tuning . Practical understanding of RAG systems using LangChain and vector databases (e.g., FAISS , Chroma , Pinecone ). Hands-on experience in agentic AI frameworks (e.g., CrewAI , AutoGen , LangGraph , or LangChain Agents ). Knowledge of tool integration , memory management , and multi-agent orchestration . Experience deploying AI models with FastAPI , Docker , Kubernetes , or cloud-native tools . Familiarity with MLOps pipelines , CI/CD automation , and monitoring frameworks . Exposure to Generative AI APIs such as OpenAI , Anthropic Claude , Google Gemini , or Azure OpenAI . Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or Artificial Intelligence or similar technical discipline. Excellent communication, mentoring, and technical training skills. Proven experience conducting technical workshops , bootcamps , or corporate AI training programs preferred. Ready to deliver on-site and virtual training . Preferred Skills/Attributes Certifications in Machine Learning , Generative AI , or Cloud AI services . Experience developing autonomous AI agents and multi-agent ecosystems . Working knowledge of vector search optimization , knowledge graph integration , and RAG performance tuning . Understanding of AI ethics, bias mitigation , and responsible AI deployment . Enthusiasm for teaching and guiding professionals through hands-on AI and MLOps implementations . Who We Are Revature is a tech career launch pad and the largest employer of entry-level technology talent in the U.S. The company works with Fortune 500 companies, government organizations, and top systems integrators to grow their business by hiring, training, and deploying top tech talent. Our mission is to create a pathway for candidates from diverse experiences and educational backgrounds to reach their potential as technology professionals. Alumni of the Revature program work on innovative, challenging, and rewarding software development projects across the United States. Revature has trained over 7000 software engineers across 54 technical disciplines, recruited talent from 700 universities, and deployed them to blue-chip companies throughout the U.S. We have proudly committed to training 1 million developers over the next decade, helping to close the opportunity gap in tech and supporting the future of work. Revature is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, genetic information, age, marital status, protected veteran status, or disability status.

Requirements

Must-haves

Revature is rapidly growing – both domestically and internationally – and our Team plays a key role in our Corporate Department.
We’re looking for an agile and ambitious candidate who is effective in the qualities listed below, all within a rapidly growing environment.
Our ideal candidate is based near one of our central offices located in this job posting.
Experience Required : Minimum 4-5 years of professional experience in AI/ML, Data Science, or Applied Machine Learning.
Deliver engaging, project-based sessions on advanced topics in AI, LLMs, and agentic AI development .
Train and mentor learners on: Core AI/ML concepts: supervised & unsupervised learning, deep learning, and NLP.
Large Language Models (LLMs): transformer architecture, fine-tuning, and prompt optimization
Retrieval-Augmented Generation (RAG): vector databases, document retrieval, embeddings, and knowledge-grounded responses.
Agentic AI Systems: Designing and orchestrating AI agents capable of autonomous decision-making
Using LangGraph , CrewAI , or AutoGen for multi-agent frameworks
Integrating external tools, APIs, and reasoning loops for dynamic task execution
Understanding memory management , context persistence , and tool use in agent frameworks
AI Deployment & MLOps: Building scalable APIs with FastAPI or Flask
Model packaging and orchestration with Docker , Kubernetes , and CI/CD pipelines
Model tracking, experimentation, and monitoring with MLflow , Weights & Biases , or Vertex AI Pipelines.
Cloud AI Integration: deploying and managing systems on AWS (SageMaker) , Azure ML , or GCP Vertex AI.
Lead hands-on projects where learners build RAG-based chatbots , autonomous AI assistants , and deployed LLM applications .
Collaborate on curriculum development to integrate cutting-edge AI research and tools into the training modules.
Mentor learners through technical challenges, performance optimization, and model deployment.
Keep up to date with LLM , agentic AI , and generative AI innovations to ensure curriculum relevance.
Required Skills & Qualifications
Experience: 4 to 5 years in AI/ML engineering , Data Science , Applied NLP , or MLOps roles.

Nice-to-haves

Proven experience conducting technical workshops , bootcamps , or corporate AI training programs preferred.
Preferred Skills/Attributes

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

agileaimlmachine learningdockerkubernetesci/cdawsazuregcpllmpython

Platform: workday
Effort: long