Artificial Intelligence Engineer

Job Title: GenAI Engineer

Experience: 2 to 5 Years

Location: Bengaluru

Shift Time: 9AM-6PM

Job Type: Full-time

Summary

We are seeking a highly skilled GenAI /Agentic AI Engineer with 2–5 years of experience to lead the development of scalable, AI-powered systems. The ideal candidate will possess deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI orchestration. You will be responsible for designing intelligent workflows and delivering production-ready applications that integrate seamlessly with complex business processes.

Key Responsibilities

GenAI Solution Development: Lead the design and development of scalable GenAI systems, focusing on intelligent workflows and vector-based retrieval.

Agentic Orchestration: Design and implement multi-agent orchestration frameworks, including Master-slave agentic architectures.

Use-Case Execution: Build and deploy specialized solutions such as Doc & KM Intelligence bots, Summarization engines, and Multi-Modal Search & Extract tools.

Conversational AI: Develop advanced conversational insights and chatbots to drive business value.

Required Qualifications & Experience

Experience: 2–5 years of experience in AI/ML with a strong focus on GenAI and Agentic AI solutions development.

Core Technical Skills: Mastery of Python, SQL, and Large Language Models (LLMs).

AI Expertise: Hands-on experience with RAG (Retrieval-Augmented Generation), Doc & KM Intelligence bots, and Agentic AI.

Advanced Implementation: Demonstrated ability to build Multi-Modal Search, automated form filling, and conversational insight tools.

Technical Breadth: Exposure to open-source components and the ability to work across different hyper-scaler stacks.

Domain Knowledge: Preferred experience in BFSI (Banking, Financial Services, and Insurance) use cases, though not mandatory.

Preferred Skills / Certifications

Cloud Platforms: Experience with Azure and GCP stacks for AI deployment.

Automation: Proficiency in developing Workflow Automation Agents.

Orchestration: Deep understanding of Master-slave Agentic orchestration frameworks.

Engineering Quality: Strong orientation toward building production-grade, scalable AI architectures.