Senior AIOps & MLOps Engineer

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 ofAI/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 an 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. Required Skills & Qualifications Experience: 10-15 years of experience in software development, with a focus on AI/ML operations, cloud infrastructure, and DevOps practices. Python: Advanced proficiency in Python, including experience with AI/ML libraries such as TensorFlow, PyTorch, scikit-learn, and Pandas. APIs: Strong experience in designing, developing, and maintaining RESTful APIs for AI/ML model deployment and integration. ML Ops: In-depth understanding of Machine Learning Operations, including model versioning, monitoring, deployment, and automation of ML workflows. LLM Concepts: Familiarity with Large Language Models (LLMs), including experience working with transformer-based models such as GPT, BERT, or T5. Azure Cloud: Hands-on experience with Azure Cloud services (Azure ML, Azure DevOps, Azure Functions, etc.) and cloud infrastructure management. DevOps & CI/CD: Proficient in setting up CI/CD pipelines for AI/ML models and using tools like Jenkins, GitLab, or Azure DevOps for automation. Data Management & Tools: Experience working with data storage and processing tools like Azure Blob Storage, Azure SQL Database, Kafka, or similar. Version Control: Expertise with Git and version control best practices for collaborative development of AI systems. Problem Solving: Strong analytical and troubleshooting skills, with the ability to identify root causes and optimize AI/ML models and systems. Communication & Collaboration: Excellent communication skills and the ability to work effectively in a cross-functional team environment. Preferred Skills Cloud Certifications: Azure certifications such as Azure Solutions Architect, Azure AI Engineer, or Azure DevOps Engineer. Security & Compliance: Understanding of security best practices in AI model deployment and experience with secure handling of sensitive data in the cloud. Big Data Tools: Familiarity with big data processing frameworks (e.g., Apache Spark, Hadoop) and integration with AI/ML pipelines. Agile Methodologies: Experience working in Agile teams, with knowledge of Scrum, Kanban, or similar frameworks. #J-18808-Ljbffr Veriipro