Overview

Director AI Architect

Responsibilities

• Primary Skills: AI/Data Science, Machine Learning, Deep Learning, Statistics, GenAI/LLM, Natural Language Processing (NLP), Cloud computing(Azure/AWS/GCP), MLOps, CI/CD, kubernetes, Dockers/Containers, Python, R
• Design and implement scalable, robust and cost-effective ML/DL infrastructure to support end-to-end Machine Learning workflows.
• Architect and develop GenAI solutions using the latest techniques such as RAG, transformer architecture, etc. in building meaningful and intricate GenAI solutions
• Develop architecture blueprints, technical designs, and implementation plans for AI/ML projects.
• Evaluate and select appropriate AI/ML technologies, frameworks, and tools based on project requirements and industry best practices.
• Build and maintain Generative AI workflows, including model training or fine-tuning, and establishing feedback loops
• Develop and maintain CI/CD pipelines for ML models, ensuring smooth deployment and monitoring in production environments.
• Collaborate with Product Managers and stakeholders to understand their business objectives, challenges, and opportunities for leveraging AI/ML technologies.
• Provide technical guidance and leadership to cross-functional teams throughout the project lifecycle
• Collaborate with data scientists and software engineers to streamline the model development lifecycle, from experimentation to deployment and monitoring.
• Implement best practices for version control, testing, and validation of ML models.
• Ensure high availability and reliability of ML systems, including performance monitoring and troubleshooting.
• Stay current with the latest advancements in AI/ML research, methodologies, and tools, and incorporate them into our solution offerings
• Mentor junior team members and guide MLOps best practices and AI advancements.

Qualifications

• PhD in Computer Science, Statistics, Software Engineering, or a related technical field
• Minimum 10-15 years in architecting, designing, and implementing AI/ML solutions and enterprise applications
• Experience in designing and implementing ML infrastructure and MLOps pipelines.
• Hands-on experience in GenAI architectures and frameworks such as RAG, vectorization and embeddings, and multi-agent GenAI architecture solutions
• Experience working with multiple LLMs including Azure Open AI, LLAMA, Claude, Gemini, etc.
• Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Understanding of ML model lifecycle management, including versioning, monitoring, and retraining.
• Uphold ethical standards in AI, focusing on the responsible use of GenAI models, data privacy, security, and ensuring compliance with regulatory requirements like HIPAA, GDPR and CCPA.
• Proficiency in Cloud platforms such as AWS, Azure or GCP.
• Strong experience with containerization and orchestration tools like Docker and Kubernetes and CI/CD tools like Jenkins
• Proficiency in programming languages such as Python, R, SQL
• Familiarity with data engineering tools and frameworks like Apache Spark, Hadoop, or Kafka.
• Knowledge of data visualization tools and techniques like Tableau, and PowerBI.
• Excellent communication and technical documentation skills.
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.