AIML Application Developer Lead

AI/ML Application Developer Lead

Role Summary:

As the AI/ML Application Developer Lead you will be responsible for the handson 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 realtime decisionmaking. This role requires a strong background in machine learning and software development along with leadership skills to guide your team in delivering highimpact AI applications for industrial and robotics environments.

Core Responsibilities:

1. HandsOn AI/ML Application Design and Development

EndtoEnd Application Development: Take a handson role in the design development and deployment of AI/ML applications. Develop applications that are scalable maintainable and optimized for performance in realworld 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 realworld environments.

2. Application Optimization and Deployment

Optimize Models for Edge and RealTime Performance: Collaborate with the Edge AI and embedded systems teams to optimize AI/ML applications for lowlatency highperformance 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 onpremises 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 postdeployment.

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 handson support and guidance in complex problemsolving.

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 highquality 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 CrossFunctional Teams

Work Closely with Data and Robotics Teams: Collaborate with the Data Lead Robotics Vision Engineers and other crossfunctional 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 decisionmaking 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

HandsOn Exploration of Emerging Technologies: Stay uptodate 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 proofofconcept projects with an aim to translate them into productionlevel applications.

OpenSource Contribution and Community Engagement: Encourage team involvement in opensource 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

RealTime 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 uptodate 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 handson development and deployment of AI/ML applications in industrial robotics or realtime environments. Demonstrated success in leading teams to build and deploy scalable productionready AI/ML solutions.

Technical Skills: Machine Learning and AI: Deep expertise in ML/DL frameworks (e.g. TensorFlow PyTorch ScikitLearn) 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 productionlevel code. Familiarity with best practices in software engineering including modular design and code testing. MLOps and CI/CD: Handson 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:

RealTime and Embedded AI: Experience in optimizing AI models for realtime 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.

OpenSource Contributions: Demonstrated contributions to opensource AI/ML projects and an active presence in the AI/ML community showcasing ongoing learning and engagement with the latest advancements.