AI Deployment Architect

Key Responsibilities

• Design and Implementation: Develop and implement highly secure AI solutions deployment from design to execution at enterprise scale with 100s of concurrent users and high performance with quick turnaround, that align with business objectives, including creating scalable architectures and integrating AI technologies into existing systems.
• Collaboration: Work closely with stakeholders, including data scientists, engineers, and business leaders, to understand requirements and translate them into AI solutions.
• Innovation: Stay updated with the latest AI trends and technologies to continuously improve and innovate AI strategies.
• Leadership: Lead AI projects deployment ensuring timely delivery and adherence to quality standards.
• Optimization: Optimize AI models for performance and scalability, addressing any issues related to data quality, model accuracy, and system integration.

Required skills

• 8+ years of experience in designing and deploying AI models at enterprise scale with fully automated MLOps in a highly secure environment supporting large number of concurrent users.
• Very strong Graduate level degree in Computer Science/or other engineering discipline from top tier schools
• AI Architecture and Pipeline Planning: Expertise in designing and planning AI workflows and pipelines, with a deep understanding of the components and architectural trade-offs involved in data management, governance, model building, deployment, and production workflows.
• Software Engineering and DevOps: Strong knowledge of software engineering and DevOps principles, including experience with DevOps workflows and tools such as Git, containers, Kubernetes, and CI/CD (Continuous Integration/Continuous Deployment).
• Data Science and Advanced Analytics: Proficiency in advanced analytics tools (e.g., SAS, R, Python) and machine learning frameworks (e.g., TensorFlow), along with knowledge of machine learning techniques (e.g., random forests, neural networks).
• Technical Proficiency: Proficient in programming languages such as Python, R, and Java.
• Analytical Thinking: Excellent problem-solving skills with the ability to analyze large datasets and extract meaningful insights.
• Project Management: Proven experience in managing complex AI projects, including planning, execution, and monitoring.
• Communication: Strong ability to communicate technical concepts to non-technical stakeholders effectively.
• Adaptability: Ability to quickly adapt to new challenges and technologies in a fast-paced environment.

Tools & Technologies

• Machine Learning Frameworks: TensorFlow, PyTorch, Keras.
• Programming Languages: Python, R, Java, C++.
• Data Management: Experience with SQL and NoSQL databases, and data preprocessing tools.
• Cloud Platforms: Strong Proficiency in AWS, Google Cloud, and Azure.
• Development Tools: Expertise with Jupyter Notebooks, Git, Docker, and Kubernetes.