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 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.