Job Description

• Establish AI Development Standards: Design and implement industry best practices for building, testing, and deploying scalable AI solutions(RAG, Prompting, Chat Completion, Multi-agent, Fine-Tuning), particularly focusing on generative models and large language models (LLMs) using both proprietary and open-source frameworks.
• Manage Infrastructure: Oversee the computing environment required for machine learning operations, ensuring efficient use of cloud platforms, data storage, and computational resources.
• Automate Deployment Processes: Develop tools and frameworks to streamline the deployment of machine learning models into production, ensuring reliability and efficiency.
• Version Control & Model Lifecycle Management: Implement structured processes for tracking different iterations of AI models, ensuring reproducibility, transparency, and proper documentation.
• Monitor & Maintain Model Performance: Set up monitoring systems to track the accuracy, health, and reliability of deployed models. Implement strategies for ongoing improvements, retraining, and updates as needed.
• Collaborate with Engineering & Data Teams: Work closely with data scientists, ML engineers, and software developers to seamlessly integrate AI models into applications, ensuring compatibility and scalability.
• Optimize Performance & Scalability: Enhance algorithms, infrastructure, and machine learning workflows to improve efficiency, minimize latency, and tackle scalability challenges.
• Implement CI/CD for ML Pipelines: Develop and maintain continuous integration and deployment pipelines tailored to AI/ML workflows, automating testing, validation, and rollout processes.
• Diagnose & Resolve Technical Issues: Identify and troubleshoot problems in models, data pipelines, and infrastructure to minimize disruptions and ensure system reliability.
• Hands-on Front-End Development: Possess practical experience in front-end technologies like React.js or Node.js, enabling seamless integration of AI solutions into interactive user interfaces.
• Document & Share Knowledge: Maintain detailed documentation of processes, configurations, and best practices to support team collaboration and smooth onboarding of new members.
• Stay Ahead of Industry Trends: Continuously research and adopt the latest advancements in AI, ML, and generative models to drive innovation and operational improvements.
• Engage in Cross-Team Collaboration: Partner with stakeholders across data science, engineering, operations, and business teams to align AI initiatives with broader organizational goals.

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