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 solid 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.

📌 AI Architect (India)
🏢 Persistent Systems
📍 India