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Job Description: AI Engineering Manager
Position Overview:
We are seeking an experienced AI Engineering Manager to lead our AI projects and drive innovation in deep learning applications. The ideal candidate will have a strong background in the latest deep learning architectures, including Convolutional Neural Networks (CNNs) and Transformers, and possess project management experience in overseeing AI initiatives.
Key Responsibilities:
· Lead the design, development, and deployment of AI solutions, ensuring alignment with organizational goals.
· Manage AI projects from inception to completion, coordinating cross[1]functional teams and resources effectively.
· Oversee the implementation of deep learning algorithms for various applications, particularly in medical image analysis.
· Mentor and guide team members, fostering a collaborative and innovative work environment.
· Ensure high-quality code development in Python, C, or C++, with a strong understanding of object-oriented programming concepts. Qualifications:
· Proven experience in managing AI projects and teams.
· Strong knowledge of deep learning architectures such as CNNs and Transformers.
· Prior experience in the medical image analysis field is preferred.
· Proficiency in programming languages including Python, C, and C++.
· Experience in people management and team leadership.
Preferred Skills:
· Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).
· Excellent communication skills and the ability to work collaboratively across teams.
· Candidates with publications in conferences such as ICCV, ICPR, ICIP, ISBI, MICCAI, MIDL, NeurIPS, and CVPR are preferred.
Job ID: 107087721
Skills:
Software Development, Debugging, Google Cloud, Computer Vision, Data Processing, Multi-Modal Large Vision Models, language modeling, Gemini connector framework, optimizing ML infrastructure, model deployment, Gemini models, ML design, AI-native front-ends, fine tuning, API-centric frameworks, generative AI techniques, model evaluation, evaluation and testing frameworks, autonomous agent architectures, prompt engineering
Skills:
secure sdlc , Automated Testing, Containers, Devops, Python development, Kubernetes, Infrastructure as Code, Generative AI, Event-driven architecture, Agentic AI frameworks, AI Governance, Model Evaluation, SRE Principles, Microservices architecture, Cloud-native architectures, Modern software engineering practices, Vector Databases, AI Agents and Multi-Agent Systems, Responsible AI, Prompt Engineering
Skills:
BigQuery, Neural Networks, Deep Learning, Tensorflow, Cloud Storage, Nlp, Git, Pytorch, MLops, Docker, Keras, DataFlow, Python, Computer Vision, Agentic AI frameworks, LLMOps, API-based deployments
Skills:
data engineering , Software Development Life Cycle, Agile Methodologies, Full Stack Java, Automation, Application Resiliency, Continuous delivery methods, AI-assisted development, Ai, Security, Cloud native experience
Skills:
Machine Learning, Network Security, Soc, Hipaa, Python, Oltp