Company Description

ExC Academy is the learning and talent development division of Exalogic Consulting, focused on preparing enterprise-ready professionals for the digital economy.

The academy bridges the gap between academic education and industry expectations through practical, project-based, mentor-led training. Programs span SAP, Artificial Intelligence, Enterprise Applications, Data Analytics, Cloud Technologies, and Digital Transformation, with curricula designed around current enterprise and technology requirements.

Learners gain hands-on exposure to modern technology stacks, enterprise platforms, production-oriented architectures, and real-world business use cases.

ExC Academy is committed to helping professionals build future-ready careers by developing practical capabilities, solving real business challenges, and adapting to evolving technologies.

Role Description The AI Consultant – Trainer is a part-time, remote role responsible for delivering an advanced, hands-on Generative AI program covering the complete journey from foundational LLM application development to enterprise-grade Agentic AI systems.

The trainer will guide learners through practical implementation of Python, asynchronous AI APIs, FastAPI, Docker, advanced prompt engineering, Enterprise RAG, vector databases, LLM fine-tuning, Agentic AI, multi-agent orchestration, LLMOps, AI security, automated evaluation, and production deployment .

The role requires conducting live online sessions, technical demonstrations, workshops, coding exercises, project reviews, assessments, and mentoring sessions. The trainer will help learners move beyond basic AI demonstrations and understand how to architect, build, evaluate, monitor, secure, and deploy production-ready AI applications.

A key responsibility of this role is mentoring learners through an enterprise capstone involving a secure multi-agent data analysis platform using CrewAI, LangChain, FastAPI, Docker, Milvus, PGVector, LangFuse, and RAGAS .

Key Responsibilities

- Deliver instructor-led training on Generative AI, Large Language Models, Enterprise RAG, Agentic AI, LLMOps, AI security, evaluation, and deployment .
- Teach production-oriented Python development, including asynchronous programming using asyncio and non-blocking LLM streaming applications.
- Train learners to design and build robust AI APIs using FastAPI and Pydantic , including structured request and response handling.
- Demonstrate containerization of AI applications using Docker , including deployment-oriented and optimized container configurations.
- Explain foundational LLM concepts including tokenization, context windows, system and user roles, structured context engineering, and structured outputs.
- Teach advanced prompt engineering approaches including Few-Shot prompting, ReAct-style reasoning workflows, structured JSON generation, and tool-oriented prompting patterns .
- Introduce learners to Model Context Protocol (MCP) concepts for decoupling AI tools from individual models.
- Guide learners in designing enterprise-grade Retrieval-Augmented Generation (RAG) systems using document ingestion pipelines, semantic chunking, metadata enrichment, hybrid search, re-ranking, and iterative retrieval strategies.
- Train learners on enterprise vector infrastructure including Milvus and PostgreSQL with PGVector .
- Demonstrate orchestration of LLM and retrieval pipelines using LangChain .
- Explain when organizations should choose prompt engineering, RAG, or model fine-tuning for specific business requirements.




- Teach dataset preparation and parameter-efficient fine-tuning techniques including PEFT, LoRA, and QLoRA .
- Provide hands-on training using Unsloth for memory-effective fine-tuning of open-source language models.
- Guide learners through exporting fine-tuned models into deployment-ready formats such as GGUF and Safetensors .
- Teach learners how to design autonomous AI agents with memory, tool-calling capabilities, and structured task execution.
- Deliver practical sessions on multi-agent orchestration using CrewAI , including agent personas, task delegation, sequential execution, and hierarchical execution.
- Explain and demonstrate Human-In-The-Loop workflows , approval checkpoints, budget controls, failure handling, and long-running agent workflow safeguards.
- Teach production-level LLMOps and AI observability using LangFuse and OpenTelemetry-based tracing.
- Guide learners in monitoring prompts, token consumption, latency, application cost, and individual AI execution traces.
- Teach AI security practices including prompt injection protection, PII masking, data anonymization, and least-privilege tool execution .
- Deliver practical sessions on evaluating AI applications using RAGAS , including Faithfulness, Answer Relevance, Context Recall, and Context Precision.
- Explain automated test dataset generation and continuous evaluation approaches for preventing AI application regressions.
- Mentor learners through hands-on assignments, mini-projects, debugging sessions, and the final enterprise capstone project.
- Review learner code, architecture decisions, project submissions, and technical documentation, and provide actionable feedback.
- Collaborate with the ExC Academy academic team to improve course delivery, assessments, labs, project requirements, and learning outcomes.
- Periodically update examples, demonstrations, and learning material based on evolving Generative AI technologies and enterprise implementation practices.

Technical Coverage The trainer should be comfortable teaching and demonstrating most of the following technologies and concepts: Programming & Backend

- Python 3.11+
- Asyncio
- FastAPI
- Pydantic
- REST APIs
- Streaming APIs

Containerization & Deployment

- Docker
- Containerized AI applications
- Microservice-oriented deployment concepts

Generative AI & LLM Development

- Large Language Models
- Tokenization
- Context windows
- Prompt engineering
- Few-Shot prompting
- ReAct workflows
- Structured outputs
- JSON schema-based generation
- Model Context Protocol concepts

LLM Frameworks

- LangChain
- Structured LLM workflows
- Tool calling
- Memory and orchestration patterns

Enterprise RAG

- Document ingestion
- Semantic chunking
- Parent-child document splitting
- Metadata enrichment
- Embeddings
- Vector search
- Hybrid search
- BM25
- Dense retrieval
- Cross-Encoder re-ranking
- Self-RAG concepts

Vector Databases

- Milvus
- PostgreSQL
- PGVector

LLM Fine-Tuning

- Dataset preparation
- Instruction datasets
- PEFT
- LoRA
- QLoRA
- Unsloth
- GGUF
- Safetensors

Agentic AI

- Autonomous agents
- Tool calling
- Agent memory
- CrewAI
- Multi-agent systems
- Sequential workflows
- Hierarchical agent workflows
- Human-In-The-Loop systems
- Agent failure handling

LLMOps & Observability





- LangFuse
- OpenTelemetry
- Prompt monitoring
- Token monitoring
- Latency tracking
- Cost tracking
- AI application tracing

AI Security

- Prompt injection prevention
- PII masking
- Data anonymization
- Tool permissions
- Least-privilege execution

AI Evaluation

- RAGAS
- LLM-as-a-Judge concepts
- Faithfulness
- Answer Relevance
- Context Recall
- Context Precision
- Automated evaluation pipelines
- Regression testing for AI applications

Capstone Mentoring Responsibility The trainer will guide learners in architecting and implementing an Enterprise-Grade AI Agentic Platform . The capstone should enable learners to understand how to:

- Upload and process internal enterprise documents.
- Dynamically parse, chunk, embed, and index enterprise data.
- Store and retrieve information using Milvus and PGVector .
- Build a multi-agent workforce using specialized Researcher, Coder, and Reviewer agents .
- Allow agents to interact safely with enterprise knowledge sources and controlled execution environments.
- Build the backend using FastAPI .
- Deploy the system within a Docker-based environment .
- Trace and observe application and agent behavior using LangFuse .
- Evaluate application quality and retrieval performance using RAGAS .

Qualifications
- Strong hands-on experience in Generative AI, Large Language Models, Agentic AI, Machine Learning, or AI application development .
- Solid proficiency in Python and experience building backend applications or AI APIs.
- Practical experience with FastAPI or comparable Python API frameworks.
- Good understanding of asynchronous programming and production-oriented AI application architecture.
- Hands-on experience with LangChain or comparable LLM orchestration frameworks .
- Practical experience designing and implementing Retrieval-Augmented Generation systems .
- Understanding of embeddings, semantic search, vector databases, document processing, retrieval pipelines, and re-ranking approaches.
- Experience with vector databases such as Milvus, PGVector, Pinecone, Weaviate, Qdrant, Chroma, or equivalent systems , with Milvus or PGVector preferred.
- Understanding of LLM fine-tuning , including LoRA, QLoRA, PEFT, dataset preparation, and open-source models.
- Experience with Unsloth or comparable model fine-tuning frameworks is highly beneficial.
- Practical knowledge of Agentic AI architectures , tool calling, autonomous agents, workflow orchestration, and multi-agent systems.
- Experience with CrewAI or similar multi-agent orchestration frameworks is highly desirable.
- Familiarity with Docker and containerized application deployment .
- Understanding of AI observability and LLMOps practices, preferably using tools such as LangFuse and OpenTelemetry .
- Knowledge of AI security concepts including prompt injection risks, PII protection, tool permissions, and secure AI application design.
- Understanding of systematic LLM and RAG evaluation methodologies, preferably using RAGAS or equivalent evaluation frameworks .
- Ability to explain complex AI architectures in a practical, structured, and easy-to-understand manner.
- Experience conducting technical training, mentoring, workshops, bootcamps, corporate training, or developer enablement programs.
- Ability to review learner code, troubleshoot technical challenges, and provide constructive project feedback.
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Software Engineering, or a related discipline , or equivalent relevant industry experience.

📌 Artificial Intelligence Consultant - Trainer (India)
🏢 ExC Academy
📍 India