Embedded Systems Trainer

Job Description

Location: Hyderabad Employment Type: Onsite ? Contractual (1 Year) Experience: Minimum 6 years in Embedded Systems, Hardware?Firmware Development, and AI Integration About ZenV Innovations ZenV Innovations is a DeepTech + AI software company building the next generation of intelligent, secure, and scalable systems. We design and develop AI-first SaaS platforms, advanced embedded hardware, and data-driven software that help organizations work smarter, faster, and safer. Our expertise spans artificial intelligence, cybersecurity, cloud computing, and hardware engineering ? enabling seamless integration between devices, data, and digital ecosystems. From designing custom hardware and firmware to delivering adaptive, AI-enabled software platforms, we create end-to-end solutions that empower enterprises, governments, and communities to operate with precision and trust. At ZenV, we don?t just build technology ? we cultivate intelligence that learns, protects, and evolves. Every creation reflects our belief in conscious engineering, where innovation meets integrity, and systems are designed to adapt and endure. We engineer the future consciously, guided by our core philosophy ? Think. Adapt. Evolve. Role Overview We are seeking a passionate and dynamic Embedded Systems Trainer who can demonstrate, mentor, and guide engineering students through practical, real-time project learning. This role is ideal for professionals with strong expertise in embedded hardware and firmware development , along with working knowledge of AI integration , who can lead hands-on workshops, hackathons, and technical sessions across academic and corporate environments. The position involves mentoring B.Tech and M.Tech students specializing in EIE, Embedded Systems, and VLSI , enabling them to gain exposure to real-time systems and applied engineering practices. Key Responsibilities Deliver demonstration-led, practical training sessions on embedded systems design, development, and debugging. Conduct hands-on workshops, hackathons, and lab sessions in both academia and corporate settings. Mentor students through end-to-end real-time projects , emphasizing practical implementation and problem-solving. Collaborate with R&D and training teams to design structured project modules, lab content, and technical documentation. Develop training material, project templates, and evaluation frameworks aligned with ZenV?s DeepTech learning standards. Introduce students to AI and machine learning applications within embedded systems where relevant. Manage training schedules, coordinate with university teams, and provide structured feedback to learners. Lead and support training assistants or project teams during hands-on sessions. Required Skills & Expertise Strong knowledge of Embedded Hardware & Firmware Development (e.g., STM32, ESP32, ARM Cortex, Raspberry Pi, Jetson, Arduino). Proficiency in C/C++, Python, RTOS , and communication protocols (UART, SPI, I2C, CAN, etc.). Understanding of AI/ML integration in embedded systems (e.g., TensorFlow Lite, Edge AI) is a plus. Familiarity with PCB design, circuit prototyping, and hardware troubleshooting. Proven experience in conducting workshops, hackathons, and technical training programs in academia or corporate setups. Excellent presentation, communication, and mentoring skills. Team leadership or coordination experience is an added advantage. Qualifications Bachelor?s or Master?s degree in Electronics & Instrumentation (EIE), Embedded Systems, VLSI Design, or related disciplines. Minimum 6 years of combined experience in embedded systems, firmware development, and technical training. What We Value A passion for demonstration-based teaching and practical learning. The ability to translate complex engineering concepts into real-world understanding. A commitment to building technical depth and confidence among students. Alignment with ZenV?s philosophy ? Think. Adapt. Evolve. Compensation & Engagement Type: 1-Year Onsite Contractual Position Location: ZenV Innovations, Hyderabad Competitive compensation based on expertise and training scope.