Ai & Simulation Focus Trainer

Trainer Profile – AI & Simulation Focus Role Purpose To train the learners in advanced simulation environments, coding practices, and AI model integration. The role combines hands-on technical instruction with career mentorship, preparing participants to apply cutting-edge AI and simulation tools in real-world projects. Language: English  Candidates should be from Middle east country. Cohort Schedule Enrolment period: August 24 - September 11 Orientation sessions period*: August 27, September 3, September 10 Course starts on: September 14 First Instructor-led session: September 15 Second Instructor-led session: September 17 Third Instructor-led session: September 22 Fourth Instructor-led session: September 24 Closing Ceremony: October 8 Sessions covered in given time zone: 3 PM GMT  Session Details: 4 Technical sessions (1.5 hours each),3 opening and 1 closing session (1 hour each)  Language: English  Training: Online live sessions Cohort Start: Mid-August  Key Responsibilities Trainer Duties Audience: MEA Students /Adult learners Conduct Live Sessions: Deliver mandatory course modules in real-time, ensuring active learner participation. Use interactive teaching methods (Q&A, coding walkthroughs, breakout discussions).  Teach fundamentals of TORCS, including environment setup, race simulation, and reinforcement learning applications. Guide learners in customizing TORCS for AI experiments. Python Coding Instruction: Lead live coding labs covering Python fundamentals, AI/ML libraries, and API integration. Provide debugging support during sessions to reinforce practical learning. TORCS Simulation Demonstrations: Show learners how TORCS works in practice — from installation and configuration to running simulations. Demonstrate reinforcement learning experiments within TORCS, guiding learners step-by-step. Encourage learners to customize TORCS environments and apply AI models to racing simulations. Granite Models Integration (LLM & GenAI): Conduct live tutorials on integrating Granite LLMs with Python applications. Showcase prompt engineering, fine-tuning, and deployment in real-world scenarios. Demonstrate how Granite models can interact with TORCS simulations for AI-driven decision-making.  Technical Environment Setup: Ensure participants successfully set up the required technical environment, including Python, TORCS simulator, OLLAMA, and Granite modules.  Project Support: Assist teams in developing their capstone project, including modifying Python code and implementing improvements in the car simulation TORCS.  Technical Troubleshooting: Provide support to resolve issues related to the simulator, coding errors, environment setup, and Granite model integration. Project Submission Support: Guide learners on preparing and submitting their project deliverables, including GitHub code, presentations/videos, and reflection forms.  Technical Knowledge Requirements TORCS: Installation, configuration, reinforcement learning integration, simulation customization. Python: Strong coding skills, AI/ML libraries, API integration, automation scripts. Granite Models (LLM & GenAI): Model deployment, fine-tuning, API usage, integration with applications. AI/ML Concepts: Neural networks, reinforcement learning, natural language processing. Tools: GitHub, Docker, cloud platforms (AWS, Azure, IBM Cloud). Key Competencies Ability to translate complex AI concepts into practical training modules. Strong mentorship mindset: guiding learners through both technical and career challenges. Hands-on expertise in simulation + AI integration projects. Excellent communication and problem-solving skills. Educational Qualifications Trainer Role Degree Requirements: Bachelor’s or master’s in computer science, Artificial Intelligence, Data Science, Robotics, or Automotive Engineering. Specialization in Machine Learning, Simulation Systems, or Sports Analytics is highly desirable. Certifications (preferred): Python programming certifications (PCEP, PCAP, or equivalent). AI/ML certifications (TensorFlow, PyTorch, IBM AI Engineering). Simulation or automotive AI certifications (if available). Cloud certifications (AWS, Azure, IBM Cloud) for deploying Granite models.   Background & Experience Trainer Background 7+ years of hands-on coding and training experience in Python, TORCS (CAR SIMULATOR) and LLM (AI Gen), GIT. Practical exposure to TORCS car simulator — installation, customization, reinforcement learning integration, and AI racing experiments. Experience with other car simulator AI platforms (e.g., CARLA, OpenAI Gym automotive environments, Unity ML‑Agents for sports simulations). Proven track record of conducting live technical training sessions (workshops, bootcamps, corporate training).