Senior GEN AI Engineer- Pune

Prodapt Solutions; Pune District; Presencial; INR 1.500.000 - 3.000.000; Jornada completa

Descripción de la vacante

Prodapt Solutions is seeking a skilled AI/ML engineer to design, implement, and deploy production-grade applications. You will build LLМ-powered systems, RAG pipelines, and agent-based architectures while collaborating with data engineers and architects to integrate AI into enterprise solutions.

The role focuses on translating customer needs into scalable AI workflows, deploying on cloud platforms, and ensuring reliability, performance, and maintainability across complex environments.

Formación

• Proven experience in building production-grade AI applications.
• Strong programming skills in Python and JavaScript.
• Experience with LLMs, RAG pipelines, and AI workflow integration.

Responsabilidades

• Engage with customers to understand business challenges, requirements, and constraints.
• Translate requirements into solution designs, workflows, and implementation plans.
• Design, develop, and deploy production-grade applications using Python/JavaScript.

Conocimientos

Python

JavaScript

LLM development

MLOps

Cloud platforms

RAG pipelines

AI integration

Herramientas

AWS

Azure

GCP

Descripción del empleo

Role & responsibilities

• Engage directly with customers to understand business challenges, technical requirements, and operational constraints
• Translate customer requirements into solution designs, technical workflows, and implementation plans
• Design, develop, and deploy production-grade applications using Python, JavaScript, or related technologies
• Build and integrate LLM-powered applications, including conversational systems, automation workflows, and knowledge-based systems
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding strategies, retrieval optimization, and response orchestration
• Build and manage agent-based architectures, including task orchestration, tool integration, and execution flows
• Design and maintain evaluation frameworks to measure model quality, retrieval effectiveness, and output reliability
• Implement and manage MLOps/LLMOps pipelines covering deployment, monitoring, versioning, rollback, and lifecycle management
• Develop and deploy applications in cloud environments such as AWS, Azure, or GCP
• Collaborate with data engineers, architects, and technical leads to integrate AI workflows into enterprise systems
• Identify technical risks and implementation bottlenecks, proposing mitigation strategies proactively
• Balance rapid prototyping with production-readiness, ensuring quality, scalability, and maintainability
• Support optimization of delivery workflows by leveraging AI tools to improve engineering productivity and operational efficiency