AIOps MLOps Architect c2c requirements Fremont, CA

Role: AIOps MLOps Architect
Location: Fremont, CA
Duration: Long Term

Job Description:
 

Strong background in Python, API development, Large Language Models (LLM) concepts, ML Ops, Azure Cloud and AI operations with  10-15 years of experience working on advanced AI/ML systems, cloud infrastructure, and API integrations, with a focus on operationalizing AI models and maintaining robust systems for AI-driven applications. This role requires a combination of technical expertise in cloud computing, machine learning, and software engineering. Collaborate with IT operations and business teams to support business user issues, requests, Production support and deployments; advocate best practices and recommend technical solutions for improvements in usability of application and systems performance

Key Responsibilities:
• Technical Operations: Review, Implement and support enterprise-level AI platforms and services to drive IT operation excellence. Ensuring that new use cases are onboarded smoothly and operationalized
• Optimization: Analyze business processes to identify areas for automation and work with business stakeholders and IT teams to determine requirements and design software bots to reduce operational toil.
• AI Ops & Model Deployment: Lead the operationalization and deployment of AI/ML models into production environments, ensuring they are highly available, scalable, and performant. Implement and monitor Continuous Integration (CI) and Continuous Deployment (CD) pipelines.
• Python Development: Design and develop Python-based solutions for automating and managing the lifecycle of AI/ML models, including data ingestion, model training, and real-time prediction workflows.
• API Integration: Build and maintain robust APIs for model serving and integration with other systems. Ensure seamless communication between models, data pipelines, and consumer applications.
• LLM Concepts and Implementation: Apply knowledge of Large Language Models (LLMs) to develop AI-driven applications and services, ensuring models are optimized and performing efficiently in production.
• ML Ops: Implement and maintain Machine Learning Operations (ML Ops) practices for version control, monitoring, logging, and debugging of AI/ML models in production. Support model retraining, versioning, and A/B testing.
• Cloud Infrastructure: Leverage Azure Cloud services for hosting and scaling AI applications, ensuring security, compliance, and performance. Implement infrastructure as code (IaC) using tools like Azure DevOps.
• Collaboration: Work closely with backend engineers, data engineers/developers, infrastructure engineers , operational SMEs and business stakeholders to tackle evolving challenges in the field of AI/ML  to ensure AI solutions meet business requirements and performance benchmarks.
• Monitoring & Optimization: Continuously monitor the performance of deployed AI models and optimize them for efficiency, cost-effectiveness, and accuracy. Implement alerting and logging mechanisms by scripts or through observability solution.
• Documentation & Best Practices: Document AI Ops processes, Use cases, tools, and workflows. Establish and enforce best practices for managing AI models in production environments.