Description

Define and drive the AI/ML architecture and roadmap, including traditional machine learning and Generative AI use cases.

This role is on-site.

Responsibilities

• Design end-to-end AI solutions covering data ingestion, feature engineering, model training, inference pipelines, and monitoring frameworks.
• Lead the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) using LangChain, LangGraph, or similar tools.
• Guide engineering teams in implementing AI components, emphasizing scalability, reliability, and performance optimization.
• Partner with DevOps to establish CI/CD pipelines for AI, including model versioning, deployment automation, and A/B testing.

Required Skills

• 10+ years of experience leading AI/ML architecture and strategy in enterprise environments.
• Strong expertise in LLMs, RAG frameworks, and generative AI techniques.
• Hands-on experience with AI/ML tools: TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph.
• Agentic AI experience: design and deliver tracing frameworks and LLM observability solutions.
• Deep understanding of data workflows, feature engineering, and model evaluation.
• Knowledge of cloud platforms (AWS, Azure, GCP) tailored for AI deployment.
• Experience developing CI/CD pipelines for AI/ML with model versioning and monitoring.
• Familiarity with model governance, security, explainability, and ethical AI standards.

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