AI Architect

Key Responsibilities

• Design and develop advanced Machine Learning models across NLP, optimization, predictive modeling, and statistical learning.
• Own end-to-end MLOps pipelines , including data ingestion, model training, deployment, monitoring, and CI/CD.
• Collaborate with Engineering, Product, and Domain teams to deliver scalable, production-ready AI solutions.
• Build and scale Knowledge Graph-driven AI systems , including ontology design, graph embeddings, and reasoning.
• Develop, fine-tune, and deploy Large Language Models (LLMs) for classification, summarization, Retrieval-Augmented Generation (RAG), and agentic workflows.
• Translate complex AI/ML concepts into scalable, practical, and real-world solutions.

Core Technical Skills

Machine Learning & Statistics

• Advanced Machine Learning
• Optimization
• Supervised and Unsupervised Learning
• Predictive Modeling
• Statistical Learning

Natural Language Processing

• NLP
• Semantic Search
• Embeddings
• Text Modeling

MLOps

• MLflow
• Kubeflow
• Airflow
• Docker
• CI/CD
• End-to-End ML Pipeline Development

Programming & AI Frameworks

• Python
• PyTorch / TensorFlow
• Hugging Face
• LangChain
• Cloud Platforms

Knowledge Graphs

• RDF / OWL
• Neo4j
• Graph Machine Learning
• Ontology Design
• Graph Embeddings
• Knowledge Graph Reasoning

LLMs & Generative AI

• Large Language Models (LLMs)
• Transformers
• LLM Fine-Tuning
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Agentic AI Workflows

Candidate Profile

The ideal candidate should have strong hands-on experience in AI/ML development and production deployment , with the ability to work across Machine Learning, NLP, LLMs, MLOps, and Knowledge Graph technologies. The candidate should also be capable of translating complex technical concepts into scalable, production-ready, real-world AI systems .