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 Dev Ops.
• 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.

• Experience:10-15 years of experience in software development, with a focus on AI/ML operations, cloud infrastructure, and Dev Ops practices.
• Python:Advanced proficiency in Python, including experience with AI/ML libraries such as Tensor Flow, 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 Dev Ops, Azure Functions, etc.) and cloud infrastructure management.
• Dev Ops & CI/CD:Proficient in setting up CI/CD pipelines for AI/ML models and using tools like Jenkins, Git Lab, or Azure Dev Ops 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.

• Cloud
  
  Certifications:
  
  Azure certifications such as Azure Solutions Architect, Azure AI Engineer, or Azure Dev Ops 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.