Job description for AI Engineer at Mitra Cakra Digital
Role Description
As an AI Engineer (Computer Vision Focus), you will be a key driver in integrating Artificial Intelligence capabilities into both our existing product lines and new client solutions. Your primary focus will be on Computer Vision (CV), designing, developing, and deploying robust CV models for tasks such as object detection, image classification, and visual data analysis. You will also be instrumental in orchestrating complex AI workflows that combine Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) techniques, ensuring seamless integration and high performance in a consulting and system integrator environment. This role requires a strong balance of theoretical knowledge and practical implementation skills.
Key Responsibilities
1. AI Solution Development & Implementation
- Computer Vision Specialization: Design, train, test, and deploy state-of-the-art Computer Vision models (e.g., YOLO, R-CNN, segmentation models) tailored for various business use cases (e.g., quality control, security, asset tracking).
- AI Workflow Orchestration: Develop and maintain end-to-end AI pipelines, integrating CV models with GenAI models (LLMs/VLMs) and RAG frameworks to create sophisticated, context-aware applications.
- Model Deployment: Work closely with MLOps/DevOps teams to ensure models are deployed efficiently, scalable, and maintainable in cloud and on-premise environments (e.g., using Docker, Kubernetes, cloud services).
2. Research & Innovation
- Feasibility Study: Conduct research into new and emerging AI/CV technologies (including specialized hardware like GPUs/TPUs for inference) to assess their potential for PT. Mitra Cakra Digital's product portfolio and client projects.
- Prototyping: Build quick proof-of-concepts (PoCs) and prototypes to demonstrate the value and technical feasibility of AI/CV solutions to internal stakeholders and clients.
3. Collaboration & Consulting
- Cross-Functional Teamwork: Collaborate with Software Developers, System Architects, and Project Managers to define requirements and integrate AI solutions into larger systems.
- Technical Documentation: Create clear, comprehensive documentation for models, pipelines, and APIs to facilitate knowledge transfer and future maintenance.
Qualifications
A. Core Technical Skills (Must-Have)
- Programming Proficiency: Expert in Python and its data science ecosystem (Pandas, NumPy).
- Computer Vision Expertise: Strong theoretical and practical knowledge of Computer Vision algorithms, techniques, and deep learning frameworks (e.g., PyTorch or TensorFlow/Keras).
- CV Libraries: Hands-on experience with popular CV libraries (e.g., OpenCV, PIL, scikit-image).
- GenAI/LLMs/RAG: Basic to intermediate understanding of Generative AI, Large Language Models (LLMs), Vector Databases, and the implementation of RAG architectures.
- MLOps Fundamentals: Experience with version control (Git) and familiarity with CI/CD, containerization (Docker), and model deployment frameworks.
B. Preferred/Bonus Skills (Good-to-Have)
- Experience in a consulting or system integrator environment, handling multiple, diverse projects.
- Familiarity with cloud-based AI services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
- Experience with optimizing models for edge devices (e.g., ONNX, TensorRT).
- Knowledge of specific vertical applications where CV is critical (e.g., Manufacturing, Retail, Security).
C. Education & Soft Skills
- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
- Problem-Solving: Strong analytical and problem-solving skills, with an ability to translate complex business problems into viable AI solutions.
- Communication: Excellent communication skills (in English and Bahasa Indonesia) to explain complex technical concepts to non-technical stakeholders (e.g., clients, project managers).
- Curiosity: High level of self-motivation and a continuous learning mindset to keep up with the rapidly evolving AI landscape.
