ThinkDeeply

Solid understanding of machine learning concepts and algorithms, with hands-on experience in developing and deploying models using libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.. Experience in developing machine learning applications tailored for Edge platforms, such as NVIDIA Jetson Orin.. Expertise in creating data synthesis systems utilizing generative AI and simulation environments, such as NVIDIA's Omniverse.. Solid understanding of DevOps practices and tools, including continuous integration/continuous deployment (CI/CD), containerization (Docker, Kubernetes), and infrastructure as code (Terraform).. Knowledge of natural language processing (NLP) techniques.

machine learning

Thinkdeeply’s vision is to make the adoption of Artificial Intelligence (AI) easy. Our mission to become a One Stop AI for customers. Using our industry starter kits, flexible AI platform and our state-of-the-art ML expertise, we help businesses become AI/ML first in a rapid fashion. We are working providing AI/ML for Autonomous Drones. We are seeking a talented and experienced Lead ML Engineer to join our team to work on the DefTech solutions. The ideal candidate will have a strong background in ML technologies. As a Lead ML Engineer, you will be responsible for designing, developing, and implementing AI-powered DefTech solutions for Autonomous Drones and Knowledge Management. Because of the nature of the job, only U.S. citizens are eligible. Experience 7+ Years Preferred Location Bozeman Key Responsibilities <ul><li>Design, Design and Deploy ML Training, Inference pipelines and models algorithms into production systems.</li><li>Architect synthetic (Tabular, Image and Video) data generation using generative AI for Autonomous Vehicles.</li><li>Develop Inference APIs and microservices to facilitate integration of AI capabilities into existing software infrastructure.</li><li>Build intuitive user interfaces and experiences that uses Streamlit</li><li>Ensure the performance, security, and scalability of AI systems.</li><li>Stay updated on the latest advancements in AI and recommend innovative solutions to enhance products and services. </li></ul>Requirements <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.</li><li>5+ years of experience in developing End-2-End ML developing and deploying models using libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.</li><li>2+ years of experience of shipping systems with following at least 2-3 of the below ML tasks:</li><li>Object Detection, Identification, Tracking, Real Time Anomaly, Tabular Data Synthesis, Image and Video Synthesis, Reinforcement Learning</li><li>2+ years of experience regarding best practices of Cloud Native Development and Deployment. </li><li>Solid understanding of machine learning concepts and algorithms, with hands-on experience in developing and deploying models using libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.</li><li>Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for building and deploying AI applications.</li><li>Strong problem-solving skills and ability to work independently or in a team environment.</li><li>Excellent communication skills and ability to effectively collaborate with stakeholders at all levels. </li></ul>Preferred Qualifications <ul><li>Experience in developing machine learning applications tailored for Edge platforms, such as NVIDIA Jetson Orin.</li><li>Proficiency in *training and fine-tuning Vision Transformers *using advanced techniques on large datasets.</li><li>Expertise in creating data synthesis systems utilizing generative AI and simulation environments, such as NVIDIA's Omniverse.</li><li>Demonstrated expertise in both supervised and unsupervised anomaly detection techniques, specifically in the Autonomous Vehicles domain.</li><li>Strong familiarity with Reinforcement Learning and its application in vision-based systems.</li><li>Proven track record of optimizing the performance and scalability of GPU-based Edge deployments, including effective management of deployment size and minimizing response times.</li><li>Solid understanding of DevOps practices and tools, including continuous integration/continuous deployment (CI/CD), containerization (Docker, Kubernetes), and infrastructure as code (Terraform).</li><li>Knowledge of natural language processing (NLP) techniques.</li><li>Experience in developing AI-driven solutions for the defense industry.</li><li>Contributions to open-source projects or authorship in prominent industry conferences or journals in related fields.</li></ul>

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