Description

Define and drive the AI/ML architecture and roadmap for enterprise-scale Generative AI and traditional machine learning solutions.

Responsibilities

• Design end-to-end AI solutions covering data ingestion, feature engineering, model training, inference pipelines, and monitoring.
• Lead the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks using LangChain or LangGraph.
• Guide engineering teams in implementing scalable, reliable AI components with performance optimization.
• Partner with DevOps to establish CI/CD pipelines for AI, including model versioning, deployment automation, and A/B testing.
• Ensure models adhere to governance, security, explainability, and regulatory compliance standards.

Required Skills

• 10+ years of experience leading AI/ML architecture and strategy in enterprise environments.
• Deep expertise in designing and deploying large-scale AI/ML solutions, including LLMs and RAG.
• Proficiency with AI/ML tools and frameworks: TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph.
• Strong understanding of data workflows, feature engineering, model training, evaluation, and deployment.
• Experience with cloud platforms (AWS, Azure, GCP) and services tailored for AI deployment.
• Familiarity with model governance, security, explainability, and ethical AI standards.
• Experience developing CI/CD pipelines for AI/ML, including monitoring and performance tuning.
• Bachelor's degree in Computer Science, Data Science, or related field.

Preferred Skills

• Advanced degree (Ph.D., Master's) in Computer Science, Data Science, AI, or related fields.
• Publications or practical contributions to AI research and open-source projects.

Key Skills

Education

Bachelor's degree