Sr. AI Engineer

Role Purpose

Develops and implements AI solutions, with strong experience in Computer Vision and NVIDIA GPU-based systems. This role is responsible for analyzing problems,

designing appropriate AI approaches, and ensuring models are effectively implemented and deployed in production environments.


Job Description

  1. Translate business and technical problems into structured AI problem definitions with clear modeling strategies.
  2. Collaborate with PMs, BSAs, and the AI Engineering team to ensure AI solutions are technically feasible, aligned with business goals, and realistic within project constraints.
  3. Develop and implement AI solutions with strong depth in Computer Vision, while remaining adaptable across predictive modeling, classification, and LLM based systems.
  4. Determine the appropriate AI approach, model architecture, and optimization strategy based on data characteristics and system constraints.
  5. Design and optimize AI models to effectively leverage NVIDIA GPU acceleration for both training and inference workloads.
  6. Identify potential risks, model limitations, and edge cases to ensure AI solutions remain scalable, maintainable, and reliable in production environments.
  7. Evaluate model performance, trade offs, and computational efficiency, particularly in GPU based environments.
  8. Ensure long term maintainability of AI solutions by considering model lifecycle, monitoring, and performance sustainability.
  9. Provide technical guidance in experimentation, model validation, and system integration to maintain solution quality and consistency.


Job Requirements

  1. Graduate from Bachelor, Diploma 3, Diploma 4 degree from IT, Computer Science or other related majors.
  2. Have a minimum 2-3 years of experience in AI / Machine Learning development, preferably with strong exposure to Computer Vision systems.
  3. Proven ownership in delivering AI solutions from experimentation to production integration.
  4. Strong analytical capability in model and method selection based on problem needs.
  5. Proficiency in Python and working knowledge of C/C++. 
  6. Experience in developing and customizing predictive and classification models.
  7. Hands-on experience with GPU accelerated AI development using NVIDIA GPUs. 
  8. Familiarity with NVIDIA AI ecosystem (DeepStream, Triton Inference Server, TensorRT, or similar GPU optimization tools). 
  9. Familiarity with video processing or streaming systems (GStreamer, FFmpeg, or similar).
  10. Experience with ML frameworks (PyTorch, TensorFlow, or equivalent) with GPU based training workflows.
  11. Understanding of multi processing, multi threading, and performance optimization concepts.
  12. Knowledge of Reinforcement Learning, stream processing frameworks (Kafka/RabbitMQ), or edge/IoT deployment is a plus.

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