Generative AI Engineer – LLM & Enterprise Knowledge Systems

at aurusai

Generative AI Engineer – LLM & Enterprise Knowledge Systems

aurusai

Company

Home

Generative AI Engineer – LLM & Enterprise Knowledge Systems

at aurusai

Posted by Uday Ayyagari

4 - 10 yrs

₹10000L - ₹1500000L / yr

Remote only

Skills

Generative AI

Retrieval Augmented Generation (RAG)

Prompt engineering

Natural Language Processing (NLP)

OpenAI

Large Language Models (LLM) tuning

Llama

Cloudera

PEFT (Parameter-Efficient Fine-Tuning)

Model-View-View-Model (MVVM)

About the Role:

We are looking for an experienced and imaginative Generative AI Architect & Engineer to lead, design, and build cutting-edge solutions using Large Language Models (LLMs) such as OpenAI’s GPT-4/5, Google Gemini, Meta’s LLaMA, and Anthropic Claude. You will spearhead the development of a corporate-wide GenAI platform that harnesses proprietary enterprise knowledge and empowers sales, operations, and other departments with instant, contextual insights, guidance, and automation.

As the GenAI leader, you will be responsible for building scalable pipelines, integrating enterprise content repositories, fine-tuning or adapting foundational models, and creating secure and intuitive access patterns for business teams. You are passionate about creating highly usable solutions that solve real-world problems, and are fluent across architecture, implementation, and MLOps best practices.

Key Responsibilities:

Architect and implement an end-to-end GenAI solution leveraging LLMs to serve as a contextual assistant across multiple business units.
Develop pipelines to ingest, clean, and index enterprise knowledge (documents, wikis, CRM, chat transcripts, etc.) using RAG (Retrieval-Augmented Generation) patterns and vector databases.
Lead fine-tuning, prompt engineering, and evaluation of LLMs, adapting open-source or commercial models to enterprise needs.
Design a secure, scalable, API-first microservice platform, including middleware and access control, integrated into corporate systems.
Work closely with sales, operations, and customer support teams to gather use cases and translate them into impactful GenAI features.
Drive experimentation and benchmarking to evaluate various open and closed LLMs (OpenAI, Claude, Gemini, LLaMA, Mistral, etc.) for best performance and cost-efficiency.
Collaborate with DevOps teams to enable MLOps workflows, CI/CD pipelines, versioning, and A/B testing for AI models.
Contribute to technical documentation, best practices, and internal knowledge sharing.

Key Qualifications:

4–5+ years of hands-on experience in AI/ML product development or applied research.
Demonstrated experience working with LLMs (OpenAI, LLaMA, Claude, Gemini, Mistral, etc.) and RAG pipelines, including vector search (FAISS, Weaviate, Pinecone, Chroma, etc.).
Strong Python skills and experience with frameworks such as LangChain, LlamaIndex, Transformers, Ray, HuggingFace, or equivalent.
Deep understanding of NLP, model fine-tuning, embeddings, tokenization, and content ingestion pipelines.
Exposure to enterprise content systems (e.g., SharePoint, Confluence, Salesforce, internal wikis, etc.) and integrating with them securely.
Solid foundation in software architecture, microservices, API design, and cloud deployments (Azure, AWS, or GCP).
Experience with security, RBAC, and compliance practices in enterprise-grade solutions.
Ability to lead projects independently and mentor junior engineers or data scientists.

How to Apply:

Submit your resume and a short technical project summary or portfolio (GitHub, Hugging Face, blog posts)

About the Role:

Key Responsibilities:

Architect and implement an end-to-end GenAI solution leveraging LLMs to serve as a contextual assistant across multiple business units.
Develop pipelines to ingest, clean, and index enterprise knowledge (documents, wikis, CRM, chat transcripts, etc.) using RAG (Retrieval-Augmented Generation) patterns and vector databases.
Lead fine-tuning, prompt engineering, and evaluation of LLMs, adapting open-source or commercial models to enterprise needs.
Design a secure, scalable, API-first microservice platform, including middleware and access control, integrated into corporate systems.
Work closely with sales, operations, and customer support teams to gather use cases and translate them into impactful GenAI features.
Drive experimentation and benchmarking to evaluate various open and closed LLMs (OpenAI, Claude, Gemini, LLaMA, Mistral, etc.) for best performance and cost-efficiency.
Collaborate with DevOps teams to enable MLOps workflows, CI/CD pipelines, versioning, and A/B testing for AI models.
Contribute to technical documentation, best practices, and internal knowledge sharing.

Key Qualifications:

4–5+ years of hands-on experience in AI/ML product development or applied research.
Demonstrated experience working with LLMs (OpenAI, LLaMA, Claude, Gemini, Mistral, etc.) and RAG pipelines, including vector search (FAISS, Weaviate, Pinecone, Chroma, etc.).
Strong Python skills and experience with frameworks such as LangChain, LlamaIndex, Transformers, Ray, HuggingFace, or equivalent.
Deep understanding of NLP, model fine-tuning, embeddings, tokenization, and content ingestion pipelines.
Exposure to enterprise content systems (e.g., SharePoint, Confluence, Salesforce, internal wikis, etc.) and integrating with them securely.
Solid foundation in software architecture, microservices, API design, and cloud deployments (Azure, AWS, or GCP).
Experience with security, RBAC, and compliance practices in enterprise-grade solutions.
Ability to lead projects independently and mentor junior engineers or data scientists.

How to Apply:

Submit your resume and a short technical project summary or portfolio (GitHub, Hugging Face, blog posts)

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About aurusai

Founded :

2018

Type :

Product

Size :

0-20

Stage :

Raised funding

About

aurus.ai is a category-defining AI-as-a-Service (AIaaS) platform with a mission to commoditize enterprise-grade AI, making powerful LLM-based capabilities accessible, relevant, and affordable for businesses of all sizes. From automating complex financial, supply chain or insurance document workflows to enabling real-time decisioning from unstructured data, our platform turns friction into functionality. We deploy a modular, functional architecture that empowers SMBs and enterprises alike to plug into the future of intelligent automation—quickly, securely, and at scale.

Candid answers by the company

What does the company do?

What is the location preference of jobs?

How many SaaS products have you led (any capacity), and shipped .

Aurus.ai is an enterprise AI platform that helps businesses automate document-heavy workflows, run predictive risk assessments, and interact with data through natural language. Its products include DoKrunch (document intelligence and automation), RiskLens (risk scoring and predictive modeling), and Cortus (a conversational interface to “speak with your data”). Aurus.ai serves SMBs and enterprises across sectors like finance, insurance, and supply chain, offering faster processing, higher accuracy, and better ROI through AI-driven automation and decision support.

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