AGIDL Software Development Lead Robotics Autonomous Systems

AGI/DL Software Development Lead – Robotics & Autonomous Systems

Role Summary:

As the AGI/DL Software Development Lead you will be responsible for overseeing the architecture development and deployment of AGI and DL frameworks for robotics applications with a focus on humanoid and quadruped platforms. This position requires a robust understanding of AGI DL robotics perception simulation environments (such as ROS and Gazebo) and advanced processing for LiDAR and point cloud data. You will lead a team of AI and robotics engineers to deliver scalable realtime AI systems that enhance automation capabilities within complex manufacturing and industrial domains.

Core Responsibilities:

1. Strategic AGI/DL Architecture and Vision

Develop and Implement AGI Architecture: Define an endtoend AGI architecture for autonomous robotic systems focusing on modular reusable components that can support humanoid quadruped and other robotics applications. Establish a vision for AGI capabilities such as reasoning planning and adaptability in a manufacturing environment.

LongTerm AI Strategy: Develop and manage a multiphase roadmap for AGI and DL initiatives balancing shortterm deliverables with longterm strategic objectives. Align AI development goals with broader organizational priorities ensuring that AGI capabilities are scalable and adaptable for diverse robotic tasks.

CrossPlatform AGI Deployment: Architect solutions that leverage both cloud and edge computing to enable decentralized processing supporting robots in dynamic environments where realtime processing is crucial.

2. AI/ML Model Development and Optimization

Model Selection and Customization: Identify and implement the most suitable deep learning models (e.g. CNNs RNNs Transformers GNNs) and AGI methodologies for specific robotic functions such as navigation manipulation and humanrobot interaction.

Generative AI and Reinforcement Learning: Explore generative AI models like GANs and VAEs for creative problemsolving and reinforcement learning (RL) approaches such as deep Qlearning and PPO for training adaptive behaviors. Develop and implement RL algorithms that can train in simulated environments and transfer learnings to physical robots.

Optimization for Performance: Leverage NVIDIA's CUDA TensorRT and cuDNN to optimize model inference on GPUs for highperformance realtime applications in manufacturing. Focus on model compression quantization and other techniques to balance speed and accuracy.

3. Robotics and Simulation Development

ROS Integration for RealTime Control: Lead the integration of AGI/DL models with ROS (Robot Operating System) to enable seamless control and perception across multiple robotic platforms including humanoids and quadrupeds. Develop ROS nodes for handling realtime data robot motion control and sensor fusion.

Simulation Environment Development with Gazebo: Use Gazebo and other simulation environments to create realistic models for testing and validating AGI and DL algorithms in virtual settings before physical deployment. Develop scenarios that simulate industrial environments for training and testing perception navigation and manipulation tasks.

Humanoid and Quadruped Robotics: Design and optimize DL models specifically for humanoid and quadruped platforms focusing on stable locomotion object interaction and adaptive learning. Address challenges in multidegreeoffreedom (DOF) control and balance for dynamic realworld scenarios.

4. Perception and Sensor Fusion

Advanced Computer Vision: Implement computer vision models for realtime object detection segmentation and scene understanding. Utilize advanced neural network architectures (e.g. YOLO Mask RCNN and Vision Transformers) for robotics applications requiring high spatial awareness.

LiDAR and Point Cloud Processing: Develop algorithms for processing and analyzing LiDAR data to support 3D mapping SLAM (Simultaneous Localization and Mapping) and obstacle detection. Implement point cloud processing pipelines for spatial understanding distance measurement and realtime navigation.

Sensor Fusion Techniques: Fuse data from multiple sensors including LiDAR cameras and IMUs to create a unified robust perception system. Use sensor fusion algorithms to improve the accuracy and reliability of spatial mapping and robot localization in changing environments.

5. Software Development and Deployment

Containerized Deployments with Docker and Kubernetes: Utilize Docker for creating containerized applications and Kubernetes for orchestrating these containers across cloud and edge devices. Develop infrastructure to deploy AGI/DL models seamlessly across different environments for scalable reliable robotic solutions.

MLOps and CI/CD Integration: Implement CI/CD pipelines in collaboration with DevOps and MLOps teams ensuring smooth deployment monitoring and retraining of models in production environments. Establish version control and model performance tracking to manage iterative improvements effectively.

NVIDIA AI Stack Optimization: Use NVIDIA tools (e.g. DeepStream SDK) to accelerate and optimize model deployment on GPUenabled devices achieving lowlatency performance for realtime applications in robotics.

6. Team Leadership and Mentorship

Lead and Develop AGI/DL Engineers: Manage a team of AI ML and robotics engineers providing mentorship on best practices technical problemsolving and professional development. Cultivate a collaborative and resultsoriented team culture.

Technical Guidance and Code Reviews: Provide regular technical feedback conduct code reviews and ensure adherence to high coding standards. Share knowledge on advanced AGI/DL concepts robotics frameworks and sensor technologies.

Goal Setting and Performance Management: Set ambitious yet attainable goals for the team tracking individual and team performance metrics. Recognize achievements and provide constructive feedback to foster ongoing improvement and engagement.

7. Innovation and Research in AGI and Robotics

Stay Current with Technological Advancements: Stay informed about the latest research and developments in AGI DL and robotics. Drive continuous innovation by exploring new methodologies tools and frameworks applicable to industrial automation and autonomous systems.

Research and Development of Novel AGI Concepts: Lead R&D initiatives into AGI areas such as transfer learning metalearning and multimodal learning. Identify ways to enhance robots' ability to learn from and adapt to novel tasks in unpredictable environments.

Open Source and Community Engagement: Support and encourage team contributions to opensource projects in AI and robotics. Participate in industry forums conferences and developer communities to share knowledge gather insights and foster collaborations.

8. Performance Security and Compliance

RealTime Optimization for Robotics: Ensure AGI/DL models meet stringent performance requirements such as low latency high accuracy and robustness to variations. Optimize models to run efficiently on both cloud and edge devices for realtime robotics applications.

Data Security and Privacy Protocols: Implement data handling protocols that prioritize security privacy and integrity especially in sensitive manufacturing environments. Address potential ethical considerations and privacy concerns with transparency.

Ethical and Regulatory Compliance: Ensure all AGI/DL models comply with relevant ethical AI standards and industry regulations. Document processes to support accountability traceability and fairness in AI decisionmaking.

Required Qualifications:

Education: Master's or Ph.D. in Computer Science Robotics AI or a related field with a focus on AGI DL and robotics.

Experience:

10 years of experience in AI DL and robotics with at least 5 years in a senior leadership role.

Proven experience in deploying autonomous robotic platforms particularly humanoids and quadrupeds in industrial environments.

Deep expertise in ROS Gazebo and DL frameworks (e.g. TensorFlow PyTorch) for robotics.

Technical Skills:

Robotics Motion and Control: Advanced understanding of multiDOF control kinematics and dynamics in humanoid and quadruped robots.

Perception Systems (LiDAR Point Cloud): Extensive experience with LiDAR sensors point cloud processing and computer vision techniques for realtime mapping and navigation.

NVIDIA Stack Proficiency: Proficiency with CUDA TensorRT and other NVIDIA tools for optimized model deployment on GPUs.

Programming: Proficiency in Python and C for highperformance computing model development and ROS integration.

Cloud and Edge Computing: Experience with AWS Azure or Google Cloud for cloudbased AI solutions and familiarity with edge computing platforms like NVIDIA Jetson for ondevice AI processing.

Reinforcement Learning Frameworks: Proficiency in OpenAI Gym Stable Baselines or similar RL libraries for training adaptive behaviors in robotic systems.

Simulation Environments: Experience with advanced simulation tools like NVIDIA Isaac Sim or Webots for realistic robotic simulations.

Sensor Fusion Libraries: Familiarity with sensor fusion libraries such as PCL (Point Cloud Library) for processing 3D point cloud data.

Deep Learning Model Optimization: Experience with ONNX (Open Neural Network Exchange) for model interoperability and optimization across different hardware platforms.

Version Control and Collaboration: Proficiency with Git and GitHub for collaborative software development and version control in largescale AI projects.

Distributed Computing: Experience with Apache Spark or similar frameworks for distributed computing in largescale machine learning applications.

Natural Language Processing: Familiarity with NLP libraries such as spaCy or NLTK for processing and understanding natural language in humanrobot interaction scenarios.

Embedded Systems: Knowledge of embedded systems programming and realtime operating systems (RTOS) for lowlevel robot control.

Data Visualization: Proficiency with data visualization libraries like Matplotlib or Plotly for analyzing and presenting complex robotics and AI data.

Preferred Qualifications:

Generative AI and LLMs: Experience with GANs VAEs and large language models for advanced robotic applications.

DevOps and MLOps: Strong experience in DevOps/MLOps practices including CI/CD model monitoring and retraining.

Agile Project Management: Proven experience with Agile methodologies particularly in AI and robotics projects.

OpenSource Contribution: Track record of contributions to opensource AI/ML and robotics projects.

Manufacturing Domain Knowledge: Familiarity with manufacturing and industrial automation requirements including regulatory compliance.