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Gen AI Engineer at BigStep Technologies Pvt Ltd
Gurgaon, haryana, India -
Full Time

Start Date

Immediate

Expiry Date

06 Jun, 26

Salary

0.0

Posted On

08 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Llm, Rag, Agentic Workflows, Open-source Models, Model Evaluation, Cloud Services, Prompt Engineering, Conversational Ai, Vector Stores, Langchain, AutoGen, Fine-tuning, Benchmarking, Observability

Industry

Software Development

Description
We are looking for a highly skilled AI Engineer with 4+ years to join our team and lead the development and deployment of advanced AI systems. You will work with large language models (LLMs), build RAG and agentic workflows, evaluate custom models, and integrate cloud-based AI services. This role offers the opportunity to contribute to cutting-edge conversational AI applications and autonomous agents. Key Responsibilities Develop and fine-tune applications using LLMs (e.g., OpenAI, Claude, Gemini). Deploy and manage custom open-source models (e.g., LLaMA, DeepSeek). Design and implement Retrieval-Augmented Generation (RAG) workflows. Build and maintain AI agentic workflows for autonomous task execution. Conduct model evaluation and performance benchmarking. Leverage cloud AI services (AWS, GCP, or Azure) for scalable deployment. [Optional] Integrate with the MCP server for interactive applications. [Optional] Work with conversational AI characters (e.g., Convai). Required Qualifications Proven experience working with foundation models and LLM APIs. Hands-on experience in deploying open-source models in production. Solid understanding of vector stores and RAG architectures. Experience with multi-step AI agent frameworks or toolchains (LangChain, AutoGen, etc). Strong knowledge of cloud services for AI workloads. Familiarity with prompt engineering, model evaluation techniques, and observability.
Responsibilities
The role involves leading the development and deployment of advanced AI systems, focusing on working with Large Language Models (LLMs) and building Retrieval-Augmented Generation (RAG) and agentic workflows. Key tasks include developing and fine-tuning applications using LLMs, deploying custom open-source models, and conducting model evaluation.