| Opportunity as AI/ML Engineer @ San Diego, CA :: Need Locals to California at California, Maryland, USA |
| Email: [email protected] |
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Greetings, This is Akilesh from ITech US inc. Given below are the details of the position with one of our clients and was wondering if you would be interested or can recommend someone who would be interested in this job. Position: AI/ML Engineer Location: San Diego, CA (Onsite) Need Locals to California Duration: 12+ Months Contract Job Description: As an AI/ML developer focusing on C/C++ and working within a DevOps environment with Kubernetes, your role involves leveraging these technologies to design, develop, deploy, and maintain machine learning models and applications. Here's a breakdown of key responsibilities and skills for such a role: Algorithm Development: Designing and implementing machine learning algorithms and models using C/C++ programming languages. This includes understanding mathematical concepts behind algorithms, such as linear algebra, probability, and statistics. Performance Optimization: Optimizing code for performance and efficiency, especially in resource-constrained environments common in embedded systems or high-performance computing (HPC) clusters. Data Processing: Writing efficient data processing pipelines in C/C++ for tasks like data cleaning, feature extraction, and transformation. This may involve libraries like OpenCV for computer vision tasks or Eigen for numerical computations. Model Training and Evaluation: Developing scripts and applications to train machine learning models using frameworks like TensorFlow or PyTorch. You'll also need to evaluate model performance using metrics such as accuracy, precision, recall, or custom metrics specific to the problem domain. Integration with DevOps Pipelines: Integrating machine learning components into continuous integration/continuous deployment (CI/CD) pipelines using tools like Jenkins, GitLab CI, or GitHub Actions. This involves automating build, test, and deployment processes for machine learning models and applications. Containerization with Docker: Packaging machine learning models and applications into Docker containers for portability and reproducibility. Docker containers encapsulate dependencies, making it easier to deploy models across different environments. Orchestration with Kubernetes: Deploying and managing containerized machine learning applications at scale using Kubernetes. This includes tasks like deploying new versions of models, scaling applications based on demand, and managing resources efficiently. Monitoring and Logging: Setting up monitoring and logging for deployed machine learning applications to track performance, detect issues, and troubleshoot problems. Tools like Prometheus and Grafana can be used for monitoring, while Fluentd or Elasticsearch can handle logging. Security and Compliance: Implementing security best practices to protect machine learning models and data, especially in production environments. This involves securing APIs, implementing access controls, and ensuring compliance with regulations like GDPR or HIPAA. Collaboration and Communication: Working closely with data scientists, software engineers, and DevOps teams to understand requirements, design solutions, and ensure smooth integration and deployment of machine learning applications Education: At least a bachelors degree (or equivalent experience) in Computer Science, Software/Electronics Engineering, Information Systems, or closely related field is required . ---- Thanks & Regards Akilesh Kumar iTech US Inc Email : [email protected] Go Green! Please do not print this e-mail unless necessary -- Disclaimer: we respect your online privacy. Under Bill 1618 Title III passed by the 105th US Congress this mail cannot be considered Spam as long as we include contact information and a method to be removed from our mailing list. If you do not want to receive emails, please reply in the subject with "REMOVE along with email address/domain. We will immediately remove and will notify you promptly. Any Inconvenience caused is regretted. --- Keywords: cprogramm cplusplus continuous integration continuous deployment artificial intelligence machine learning information technology golang California |
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| 12:11 AM 09-Feb-24 |