Description

You will architect and deploy production-grade GenAI and Machine Learning systems for internal enterprise operations.

This role is on-site.

Responsibilities

• Transform large-scale internal data (logs, tickets, telemetry) into production-ready GenAI solutions.
• Design and deploy LLM-powered assistants and predictive models on Kubernetes and distributed cloud architectures.
• Implement evaluation frameworks using RAGAS and DeepEval to ensure accuracy and latency standards.
• Partner with stakeholders to identify high-impact AI opportunities and optimize internal department OKRs.
• Deliver production-ready models for resource forecasting, system health monitoring, and automated fault diagnosis.

Required Skills

• 10+ years of end-to-end ML delivery in production environments.
• Expertise in GenAI stack: RAG, Lang Graph, Langfuse, Semantic Search, Re-ranking, and Vector Databases (OpenSearch).
• Strong background in ML and Statistics: Regression, Clustering, Neural Networks, A/B testing, and controlled experiments.
• NLP expertise with BERT, Transformers, and RASA (DIET Classifier).
• DevOps and infrastructure experience with Kubernetes, Helm, Microservices, and REST APIs.
• Bachelor’s or Master’s degree in Data Science or Computer Science.

Preferred Skills

• Experience balancing immediate internal feature requests against long-term technical health of production environments.

Key Skills

Education

Any Graduate