Description

You will build and deploy production-ready AI/ML features, data pipelines, and inference systems. Your work spans model training, GenAI application development, and scalable solution delivery.

Responsibilities

• Develop GenAI applications using prompt engineering, guardrails, and RAG pipelines.
• Implement vector search, embeddings, and retrieval mechanisms for scalable AI solutions.
• Optimize model performance and inference efficiency using quantization, batching, and fine-tuning (LoRA/PEFT).
• Build and maintain MLOps and LLMOps pipelines for continuous delivery and monitoring.
• Ensure Responsible AI practices including fairness, explainability, and safety.

Required Skills

• Strong experience in Artificial Intelligence and Machine Learning model development and deployment.
• Proficiency in Python for building end-to-end AI/ML and GenAI solutions.
• Good understanding of Generative AI concepts including LLMs, prompt engineering, and RAG pipelines.
• Experience in data ingestion, feature engineering, model training, and evaluation workflows.
• Knowledge of embeddings, vector databases, and semantic search techniques.
• Familiarity with MLOps and LLMOps practices including CI/CD, model versioning, and monitoring.
• Understanding of observability practices including metrics, logs, traces, and alerting for AI systems.
• Experience in implementing Responsible AI practices such as fairness, explainability, and bias mitigation.
• Familiarity with cloud platforms and containerized deployments for scalable AI solutions.

Key Skills

Education

Any Graduate