AI Deployment Architect

Job Description

Skills: Application Architecture, MLFlow, GenAI, Cloud Certifications, AI Certifications, CI/CD pipelines,

Job Summary - AI Deployment Architect

As AI Deployment Architect, you will leverage cutting edge technologies and best practice methodologies to build solutions that will help users across our cloud customers deploy their models efficiently and reliably.

• Develop robust and scalable solutions in the domain of AI model operations (MLOps and DevSecOps) with strong focus on next generation models.
• Build reusable frameworks and platfoms to allow data scientists, devops teams to train and deploy models.
• Bring considerable savings to clients by improving the time to market of their AI applications, whilst adhering to the operational controls and governance.
• Automate model packaging and deployment into prod environments, with traceability on model version, experimentation and control checks.
• Develop infra for centralized management of model artifacts.
• Building automated model performance monitoring services to monitor model health and report data to data scientists, model owner, and model validators.
• Enable model testing such as shadow testing and A/B testing.
• Providing security protection (malicious code execution, prompt injection, data leakage, LLM jailbreaking)

• Extensive experience writing production-ready code, hands-on experience with different programming paradigms and willingness to quickly learn and start using various technologies.
• Senior-level Python programming knowledge. Good knowledge of other programming languages is a plus.
• Extensive platform and cloud knowledge.
• Extensive practical experience with DevOps, such as building CI/CD pipelines on GitLab.
• Experience with deployment and tools such as Docker and Kubernetes.

• Knowledge of MLOps, GitOps, and Infrastructure-as-code.
• Practical experience with tools such as MLFlow, Ansible, Terraform is a strong advantage.
• Practical experience with model development, foundational models, GenAI, or tools such as sklearn, TensorFlow, PyTorch, Keras, LangChain, etc. is a plus.
• Familiarity with data engineering, data management, security and compliance is a plus.
• Enjoy working hands-on and writing code that is secure, stable, scalable and well-performing.
• BTech in Computer Science, Cloud Certifications like Azure Solutions Architect, AI Certifications like Certified Deep Learning Engineer