Roles and Responsibilities
- Implement and support enterprise AI platforms, ensuring smooth onboarding and operationalization of new use cases.
- Analyze business workflows to identify automation opportunities and collaborate with stakeholders to design software bots that reduce manual effort.
- Lead deployment of AI/ML models into production, ensuring availability, scalability, and performance; implement and monitor CI/CD pipelines.
- Design and build Python solutions to automate AI/ML lifecycle management, including data ingestion, training, and real-time predictions.
- Develop and maintain robust RESTful APIs to enable seamless integration between AI models, data pipelines, and applications.
- Apply Large Language Model concepts (e.g., GPT, BERT) to develop and optimize AI-driven applications.
- Implement model versioning, monitoring, logging, debugging, retraining, and A/B testing for AI/ML workflows.
- Utilize Azure Cloud services for hosting, scaling, and securing AI applications; employ Infrastructure as Code (IaC) using Azure DevOps or similar tools.
- Work cross-functionally with engineers, operational SMEs, and business stakeholders to ensure AI solutions align with business goals.
- Continuously track model performance and optimize for accuracy, efficiency, and cost-effectiveness using monitoring and alerting tools.
- Document workflows, processes, and use cases; establish and enforce best practices for AI/ML production environments.
Required Skills & Qualifications
- 10-15 years of software development experience focused on AI/ML operations, cloud infrastructure, and DevOps.
- Advanced Python programming skills with experience in AI/ML libraries such as TensorFlow, PyTorch, scikit-learn, and Pandas.
- Strong expertise in designing and maintaining RESTful APIs for AI/ML model deployment.
- Deep understanding of ML Ops: model versioning, deployment automation, monitoring, and workflow orchestration.
- Familiarity with Large Language Models (LLMs) including transformer architectures like GPT, BERT, or T5.
- Hands-on experience with Azure Cloud services (Azure ML, Azure DevOps, Azure Functions, etc.).
- Proficiency in DevOps and CI/CD pipeline tools (Jenkins, GitLab, Azure DevOps).
- Experience with data storage/processing tools such as Azure Blob Storage, Azure SQL, Kafka, or equivalents.
- Expertise in Git and version control best practices.
- Strong problem-solving, analytical, and troubleshooting capabilities.
- Excellent communication skills; proven ability to collaborate in cross-functional teams.
Preferred Skills
- Azure certifications (e.g., Azure Solutions Architect, Azure AI Engineer, Azure DevOps Engineer).
- Knowledge of security best practices for AI deployments and handling sensitive cloud data.
- Familiarity with big data frameworks (e.g., Apache Spark, Hadoop) and their integration with AI/ML pipelines.
- Experience working in Agile teams with Scrum or Kanban methodologies.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field preferred.