AI Architect
Persistent Systems
India
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 LearningNatural Language Processing
- NLP
- Semantic Search
- Embeddings
- Text ModelingMLOps
- MLflow
- Kubeflow
- Airflow
- Docker
- CI/CD
- End-to-End ML Pipeline DevelopmentProgramming & AI Frameworks
- Python
- PyTorch / TensorFlow
- Hugging Face
- LangChain
- Cloud PlatformsKnowledge Graphs
- RDF / OWL
- Neo4j
- Graph Machine Learning
- Ontology Design
- Graph Embeddings
- Knowledge Graph ReasoningLLMs & Generative AI
- Large Language Models (LLMs)
- Transformers
- LLM Fine-Tuning
- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- Agentic AI WorkflowsCandidate 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.