Senior Trainer – Artificial Intelligence & Machine Learning (RAG, Agentic AI & D
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:
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Core AI/ML concepts: supervised & unsupervised learning, deep learning, and NLP.
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Large Language Models (LLMs): transformer architecture, fine-tuning, and prompt optimization
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Retrieval-Augmented Generation (RAG): vector databases, document retrieval, embeddings, and knowledge-grounded responses.
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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
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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.
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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
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Experience: 4 to 5+ years in AI/ML engineering , Data Science , Applied NLP , or MLOps roles.
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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 .
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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 .
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