Job Opening: Data Science & AI Trainer Company: Analytix Learning Academy

Location:
Hyderabad, Telangana Work Mode: Classroom / Online / Hybrid (as applicable) Employment Type: Full-time / Contract (as applicable)

Experience:
3–6+ years of relevant industry or hands-on project experience Open Positions: 1 About Analytix Learning Academy Analytix Learning Academy delivers industry-oriented training programmes designed to help students and graduates develop practical, job-ready skills in Data Science, Analytics, Artificial Intelligence and emerging technologies. We are looking for an experienced Data Science & AI Trainer who can deliver hands-on technical training, mentor real-world projects, evaluate student performance and prepare learners for technical interviews. Job Summary The trainer will deliver the Data Science and AI Engineering components of our Applied Data Science & AI Engineer programme, covering Python programming, data analysis, machine learning, deep learning, Generative AI, LLM applications, RAG, Agentic AI and deployment fundamentals. The ideal candidate should have strong technical fundamentals, practical project experience, excellent communication skills and the ability to explain complex concepts to freshers and graduates with little or no prior coding experience.

Key Responsibilities
1. Technical Training & Curriculum Delivery
- Deliver structured, instructor-led training in Python programming, data analysis and AI engineering.
- Teach Python fundamentals, functions, data structures, OOP, APIs, NumPy, Pandas and data visualisation.
- Teach advanced Python practices, project structure, debugging, logging, testing and code quality.
- Deliver practical sessions on machine learning algorithms, feature engineering, model training, evaluation and tuning.
- Introduce deep learning concepts, neural networks, CNNs, transfer learning and transformer fundamentals.
- Teach Generative AI, LLM APIs, prompt engineering, context engineering and structured outputs.
- Demonstrate embeddings, vector databases, Retrieval-Augmented Generation (RAG), advanced RAG and LLM evaluation.
- Guide students in developing AI agents and multi-agent workflows using tools such as LangChain, LangGraph, CrewAI and MCP, as appropriate to the curriculum.
- Introduce model and application deployment using FastAPI, Docker, MLflow, CI/CD and cloud platforms such as AWS and Azure.
- Integrate Git,



GitHub and AI coding assistants into practical learning activities. 2. Hands-on Projects & Practical Learning
- Conduct live coding sessions, coding exercises, guided labs and real-world case studies.
- Design and evaluate module-wise mini projects and mentor-guided capstone projects.
- Guide learners through projects such as:
- Exploratory Data Analysis and business insight generation.
- Customer churn prediction using machine learning.
- Image classification using transfer learning.
- RAG-based policy or knowledge-base chatbot.
- AI application development and deployment.
- Teach students to explain their approach, justify technical decisions and communicate project outcomes.
- Maintain project repositories, documentation and portfolio standards using GitHub. 3. Assessments & Student Mentoring
- Assign daily coding tasks, practical exercises and module-wise assignments.
- Conduct weekly examinations, coding assessments and technical mock interviews.
- Evaluate assignments and projects using predefined rubrics.
- Provide constructive feedback and remedial support to students who need additional guidance.
- Track attendance, learning progress, assessment scores and project completion.
- Support students in building GitHub portfolios and preparing for technical interviews. 4. Curriculum & Training Quality
- Follow the approved curriculum, training schedule and learning outcomes.
- Prepare lesson plans, presentations, coding notebooks, datasets, assignments and project documentation.
- Incorporate current AI development practices and relevant industry case studies.
- Coordinate with the academic and placement teams to improve learner readiness.
- Use AI tools responsibly and teach students to validate AI-generated code and outputs. Required Technical Skills Core skills
- Strong Python programming and debugging skills.
- NumPy, Pandas, Matplotlib and exploratory data analysis.
- Statistics and mathematics relevant to machine learning.
- Scikit-learn and machine learning algorithms.
- Feature engineering,



model evaluation and explainability. AI engineering skills
- Practical experience with deep learning frameworks such as PyTorch or TensorFlow/Keras.
- Hands-on experience with LLM APIs, prompt engineering and embeddings.
- RAG pipelines, vector databases and LLM application development.
- Familiarity with agentic frameworks such as LangGraph or CrewAI.
- Working knowledge of FastAPI, Docker, Git/GitHub and deployment workflows.
- Exposure to cloud deployment, MLOps/LLMOps and model monitoring. Educational Qualifications
- B.
E./B.
Tech, M.
E./M.
Tech, MCA, M.
Sc. in Computer Science, Data Science, AI or a related discipline preferred.
- Equivalent practical experience and a strong project portfolio may also be considered. Experience Requirements
- Demonstrable experience building or implementing Python, ML or AI projects.
- Experience delivering technical training, mentoring students or conducting workshops is preferred.
- Candidates with industry experience and proven teaching ability are encouraged to apply. Preferred Candidate Profile
- Strong presentation, communication and classroom management skills.
- Ability to explain complex topics in straightforward, structured language.
- Comfortable teaching freshers and learners from non-programming backgrounds.
- Strong problem-solving, debugging and project mentoring capabilities.
- Willingness to continuously update technical skills in the evolving AI ecosystem. Performance Expectations
- Complete assigned modules according to the approved training plan.
- Deliver practical sessions and ensure meaningful learner participation.
- Maintain assessment records and provide timely feedback.
- Ensure students complete their mini projects and capstone milestones.
- Help learners develop demonstrable technical portfolios and interview readiness.

How to Apply
Interested candidates may share their updated resume, LinkedIn profile and GitHub/portfolio links, along with a brief description of their relevant projects. Email: [email protected] Contact: (phone hidden) (Phani Kumar) Subject Line: Application – Data Science & AI Trainer – Hyderabad Analytix Learning Academy is committed to practical, project-driven learning and industry-oriented skill development. Pay: ₹30,000.00
- ₹50,000.00 per month Work

Location:
In person

📌 Data Science Trainer (Hyderabad)
🏢 Analytix Learning Academy
📍 Hyderabad