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Vrinda International

Vrinda International

AI/ML Engineer

Pune, INNa miejscuUmowaPełny etat

Opublikowano 1 paź 2026

To stanowisko jest opublikowane w języku EN

Summary
We are seeking a highly skilled AI/ML Engineer to join our IT Services team. In this pivotal role, you will leverage advanced machine learning frameworks and cloud-native technologies to design, build, and deploy scalable artificial intelligence solutions. The position demands a deep understanding of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures to drive innovation and deliver robust technical outcomes that align with our strategic business objectives.

Responsibilities
  • Architect and implement end-to-end machine learning pipelines using Python, TensorFlow, and Kubeflow.
  • Develop and optimize Large Language Model applications, integrating RAG strategies to enhance data retrieval and response accuracy.
  • Containerize applications and manage orchestration workflows utilizing Docker and Kubernetes to ensure high availability and scalability.
  • Collaborate with cross-functional teams to integrate OpenAI capabilities and other AI services into production environments.
  • Monitor model performance and infrastructure health, ensuring continuous improvement and operational efficiency.

Requirements

Requirements:
  • Possess 4-6 years of professional experience in software development and machine learning engineering.
  • Demonstrate proven expertise with core technologies including Python, TensorFlow, Docker, and Kubernetes.
  • Show strong proficiency in building RAG systems and working with Large Language Models (LLM).
  • Have hands-on experience with Kubeflow for workflow management and model deployment.
  • Familiarity with OpenAI APIs and their integration into enterprise-grade applications is essential.

Przegląd stanowiska

Typ stanowiska

Pełny etat

Wymagane umiejętności

PythonTensorFlowKubeflowDockerKubernetesLarge Language Models (LLM)Retrieval-Augmented Generation (RAG)End-to-end machine learning pipeline architectureModel deployment (productionizing ML models)Model and infrastructure monitoring (MLOps)OpenAI API integrationCloud-native architecture designCross-functional collaboration

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