AI/ML Application Developer Lead
Role Summary:
As the AI/ML Application Developer Lead, you will be responsible for the hands-on design, development, and deployment of AI/ML applications, as well as leading a team of engineers. You will work closely with data, robotics, and software teams to build robust, scalable AI solutions that enhance autonomous capabilities, support predictive analytics, and drive real-time decision-making. This role requires a strong background in machine learning and software development, along with leadership skills to guide your team in delivering high-impact AI applications for industrial and robotics environments.
Core Responsibilities:
1. Hands-On AI/ML Application Design and Development
• End-to-End Application Development: Take a hands-on role in the design, development, and deployment of AI/ML applications. Develop applications that are scalable, maintainable, and optimized for performance in real-world industrial environments.
• Algorithm Development and Model Implementation: Actively design and implement machine learning algorithms and models based on application requirements, including supervised, unsupervised, and reinforcement learning techniques. Build models for predictive maintenance, anomaly detection, and robotic perception as needed.
• Application Prototyping and Testing: Rapidly prototype, test, and iterate on AI/ML applications to meet performance requirements. Validate models and applications through rigorous testing in both simulated and real-world environments.
2. Application Optimization and Deployment
• Optimize Models for Edge and Real-Time Performance: Collaborate with the Edge AI and embedded systems teams to optimize AI/ML applications for low-latency, high-performance processing on edge devices. Use techniques like model compression, quantization, and hardware acceleration (e.g., TensorRT) to maximize efficiency.
• Deployment and Integration: Lead and participate in the deployment of AI/ML applications across cloud, edge, and on-premises environments. Ensure seamless integration with robotics and industrial systems for consistent performance and reliability.
• MLOps and Continuous Improvement: Implement MLOps best practices, including CI/CD pipelines, model versioning, monitoring, and automated retraining to maintain and improve models over time. Actively engage in troubleshooting and optimization post-deployment.
3. Leadership and Team Management
• Mentor and Guide a Team of AI/ML Engineers: Lead and mentor a team of AI/ML engineers, fostering a collaborative environment and setting high standards for technical excellence. Provide hands-on support and guidance in complex problem-solving.
• Project and Resource Management: Manage project timelines, resource allocation, and task prioritization for the AI/ML development team. Ensure alignment with project goals and ensure timely delivery of high-quality applications.
• Promote Technical Excellence and Best Practices: Lead by example in adopting coding standards, modular development practices, and documentation. Conduct code reviews and promote knowledge sharing within the team to maintain a high level of technical quality.
4. Collaboration with Cross-Functional Teams
• Work Closely with Data and Robotics Teams: Collaborate with the Data Lead, Robotics Vision Engineers, and other cross-functional teams to ensure that AI/ML applications align with data and system requirements. Actively contribute to data preparation, feature engineering, and model integration.
• Align with Head Robotics and Edge AI Lead: Ensure alignment with the Head Robotics and Edge AI Lead on the integration of AI/ML models into robotics systems. Support the development of perception, decision-making, and control algorithms that enhance robotic autonomy.
• Collaborate with Software and Embedded Systems Teams: Work closely with software and embedded systems teams to ensure smooth deployment of AI/ML models on edge devices and embedded platforms. Address compatibility and performance issues for seamless deployment.
5. Innovation and R&D in AI/ML Technologies
• Hands-On Exploration of Emerging Technologies: Stay up-to-date with advancements in AI and ML, including deep learning, reinforcement learning, generative AI, and computer vision. Experiment with new techniques and evaluate their applicability to robotics and industrial automation.
• Drive R&D Projects: Lead research initiatives, actively participate in development, and evaluate new AI/ML techniques that could improve robotic functionality, adaptability, and intelligence. Engage in proof-of-concept projects with an aim to translate them into production-level applications.
• Open-Source Contribution and Community Engagement: Encourage team involvement in open-source projects and contribute to the AI/ML community. Participate in industry forums, conferences, and publications to stay engaged with industry trends.
6. Model Monitoring, Evaluation, and Maintenance
• Real-Time Model Monitoring and Maintenance: Develop tools and processes to monitor model performance in production environments. Actively track metrics such as accuracy, latency, and resource utilization to ensure that deployed models meet performance standards.
• Continuous Evaluation and Optimization: Regularly evaluate models for performance and adapt models as needed to handle new data or evolving operational needs. Implement feedback loops and make adjustments to maintain high levels of accuracy and reliability.
• Automated Retraining Pipelines: Build and maintain automated retraining pipelines to keep models up-to-date with incoming data. Ensure thorough documentation of models, algorithms, and application logic for reproducibility and traceability.
Required Qualifications:
• Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. Advanced degrees or certifications in machine learning or AI are preferred.
• Experience: • 8+ years of experience in AI/ML development, with a minimum of 3 years in a technical leadership role. • Proven experience in hands-on development and deployment of AI/ML applications in industrial, robotics, or real-time environments. • Demonstrated success in leading teams to build and deploy scalable, production-ready AI/ML solutions.
• Technical Skills: • Machine Learning and AI: Deep expertise in ML/DL frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn) and a broad range of algorithms, including computer vision, NLP, reinforcement learning, and anomaly detection. • Software Development: Proficiency in Python and at least one other language (e.g., C++ or Java), along with experience in developing production-level code. Familiarity with best practices in software engineering, including modular design and code testing. • MLOps and CI/CD: Hands-on experience with MLOps tools and practices, including CI/CD, model versioning, monitoring, and retraining pipelines. Proficiency with tools like MLflow, Kubeflow, or similar platforms. • Edge and Cloud Deployment: Experience with deploying and optimizing models for edge devices and cloud platforms. Familiarity with tools such as Docker, Kubernetes, and TensorRT for containerization and model optimization.
• Data Processing and Analysis: Proficiency in data manipulation and analysis libraries such as Pandas, NumPy, and SciPy. Experience with big data processing frameworks like Apache Spark or Hadoop.
• Database Management: Knowledge of SQL and NoSQL databases, with experience in data modeling and querying for AI/ML applications.
• Cloud Platforms: Familiarity with major cloud platforms (AWS, Azure, GCP) for AI/ML development and deployment, including services like SageMaker, Azure ML, or Google AI Platform.
• Version Control and Collaboration: Proficiency with Git and collaborative development platforms like GitHub or GitLab.
• API Development: Experience in designing and implementing RESTful APIs for AI/ML services.
• Data Visualization: Skills in data visualization tools and libraries such as Matplotlib, Seaborn, or Plotly for effective communication of insights.
• Parallel Computing: Understanding of parallel computing concepts and experience with libraries like CUDA for GPU acceleration in AI/ML tasks.
Preferred Qualifications:
• Real-Time and Embedded AI: Experience in optimizing AI models for real-time applications on edge or embedded systems. Familiarity with hardware accelerators and embedded libraries for deploying models on constrained devices.
• Industrial Automation and Robotics: Knowledge of industrial automation systems, robotics, and control algorithms. Practical experience deploying AI/ML applications in manufacturing or autonomous environments is highly desirable.
• Agile Project Management: Proficiency in Agile methodologies and project management tools, with experience managing and delivering AI/ML projects in a collaborative setting.
• Open-Source Contributions: Demonstrated contributions to open-source AI/ML projects and an active presence in the AI/ML community, showcasing ongoing learning and engagement with the latest advancements.