As a Deep Learning Engineer specialized in 3 D Computer Vision and Reinforcement Learning, you will work on developing, implementing, and optimizing state-of-the-art models that tackle complex challenges in spatial AI and geometric processing. You will be part of a multidisciplinary team pushing the frontiers of 3 D representation, algorithmic generation, and autonomous agents, contributing to the research, development, and deployment of large-scale AI systems capable of interpreting, manipulating, and generating complex 3 D structures.
Requisite Abilities and Skills :
Relevant Experience
1 to 4 years
Desired Education Qualification
Degree in Computer Science, Applied Mathematics, Artificial Intelligence, or a related discipline. Specialized coursework or research in 3 D deep learning, reinforcement learning, and advanced algorithmic design. Strong mathematical foundation.
Tools/Skillset if applicable
Expertise in deep learning and machine learning algorithms, particularly in the context of 3 D vision, reinforcement learning, and generative geometric models. Robust proficiency in programming languages like Python and C++. Familiarity with ML frameworks (Py Torch, Tensor Flow) and specialized 3 D/geometric processing libraries (e.g., Py Torch3 D, Open3 D, Trimesh, or similar). Exceptional problem-solving skills with a focus on building algorithmic solutions from the ground up. Familiarity with cloud-based solutions and tools (AWS, GCP, or Azure) for scalable model training and distributed computing.
Domain Knowledge
Proven experience developing state-of-the-art Deep Learning models, specifically focusing on Computer Vision, 3 D CNNs, and Geometric Deep Learning.
Strong expertise in Deep Reinforcement Learning (DRL) and developing autonomous agents for complex decision-making tasks in spatial, geometric, or simulated environments.




Hands-on experience working extensively with 3 D data representations, including 3 D meshes, point clouds, voxels, and CAD data structures (e.g., B-rep, STEP, IGES).
Deep knowledge of core mathematical algorithms, computational geometry, linear algebra, and numerical methods, with a proven ability to implement complex algorithms from scratch.
Practical knowledge of deploying custom AI models and algorithms at scale in production environments.
Job Description
Research & Development: Lead and contribute to research initiatives focused on advanced deep learning models, particularly 3 D Convolutional Neural Networks (CNNs), Geometric Deep Learning, and Deep Reinforcement Learning (DRL) for spatial problem-solving.
Model Design & Implementation: Design and implement custom neural network architectures and autonomous agents capable of interacting with, interpreting, and generating complex 3 D geometries, including CAD data and 3 D meshes.
Mathematical & Algorithmic Engineering: Develop, optimize, and implement core mathematical algorithms from first principles. Heavily utilize computational geometry, topology, and linear algebra to process spatial data natively, building proprietary solutions rather than relying on off-the-shelf commercial software.
Training & Fine-Tuning: Conduct training and fine-tuning of complex models (e.g., DRL agents, 3 D CNNs) in simulated and geometric environments, leveraging modern machine learning frameworks such as Py Torch and Tensor Flow.
Optimization: Work on scaling, optimizing, and improving the efficiency of custom algorithms and large models, including distributed training, parallelism, and hardware acceleration (GPUs, TPUs).
Deployment & Integration: Collaborate with engineering teams to integrate these 3 D deep learning models and agentic workflows into robust, scalable production systems.

📌 Aiml_3d deep learning engineer (Chennai)
🏢 Actalent
📍 Chennai