Generative AI Engineer – LLM & Enterprise Knowledge Systems at aurusai · Remote only · 4 - 10 years · ₹10000L - ₹1500000L / yr · Raised funding · Remote only · Posted 10 May 2025
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)
Shubham Vishwakarma
Full Stack Developer - Averlon
About aurusai
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
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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Job Description
We are seeking a highly skilled Senior Generative AI Engineer with strong expertise in designing, developing, and deploying enterprise-scale AI solutions using Large Language Models (LLMs) and modern Generative AI frameworks. The ideal candidate should have hands-on production experience building scalable GenAI applications, AI agents, autonomous workflows, and Retrieval-Augmented Generation (RAG) systems in cloud-native environments.
This role requires deep technical expertise in LLM orchestration, AI application architecture, prompt engineering, vector databases, MLOps, and production deployment of AI systems. Candidates should have proven experience delivering real-world AI solutions in enterprise environments with strong exposure to cloud platforms and DevOps practices.
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- Design, build, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs).
- Develop intelligent AI agents and autonomous workflows using frameworks such as LangChain, CrewAI, LangGraph, AutoGen, or similar agentic AI frameworks.
- Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search technologies.
- Work extensively on prompt engineering, tool calling, memory management, agent orchestration, and multi-agent systems.
- Integrate and manage LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar foundation models.
- Develop scalable AI services and APIs using Python and FastAPI.
- Build production-ready AI solutions with high availability, scalability, monitoring, and observability.
- Deploy and manage AI applications in cloud-native environments using Docker and Kubernetes.
- Collaborate with Data Science, ML Engineering, and DevOps teams to operationalize AI solutions.
- Implement CI/CD pipelines and automated deployment processes for AI workloads.
- Monitor model performance, latency, reliability, and operational efficiency in production environments.
- Ensure AI solutions follow enterprise security, governance, and responsible AI standards.
- Evaluate and adopt emerging Generative AI tools, frameworks, and models.
Required Skills
Generative AI & LLM Expertise
- Strong hands-on experience with Generative AI and Large Language Models (LLMs).
- Production-level experience building and deploying GenAI applications.
- Expertise in LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
- Experience with AI agents, autonomous workflows, and multi-agent architectures.
- Strong understanding of prompt engineering, embeddings, model evaluation, and LLM orchestration.
- Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar models.
RAG & Vector Databases
- Strong experience implementing RAG pipelines and semantic retrieval systems.
- Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus.
- Understanding of chunking strategies, embeddings, indexing, reranking, and retrieval optimization.
Python & AI Development
- Strong proficiency in Python.
- Experience with FastAPI for AI service and API development.
- Experience with AI/ML libraries and data processing tools such as Pandas and NumPy.
Cloud & Production Deployment
- Mandatory production experience on at least one cloud platform:
- Microsoft Azure
- Experience deploying scalable AI applications in enterprise production environments.
- Hands-on experience with Docker, Kubernetes, Jenkins, Terraform, and CI/CD pipelines.
- Strong understanding of MLOps, AI deployment lifecycle, monitoring, and observability.
Engineering & Operational Excellence
- Strong understanding of software engineering best practices.
- Experience with Git, version control, automated testing, and release management.
- Experience building secure, scalable, and high-performance AI solutions.
- Ability to troubleshoot production AI systems and optimize performance.
Preferred Skills
- Experience with AI observability and evaluation frameworks.
- Exposure to fine-tuning, PEFT, LoRA, or model optimization techniques.
- Experience with enterprise AI governance and responsible AI practices.
- Knowledge of distributed AI systems and scalable inference architectures.
- Familiarity with AI security and compliance standards.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 3–8 years of overall software engineering experience.
- Minimum 3+ years of hands-on experience in Generative AI and LLM-based application development,
- Proven track record of delivering enterprise-scale AI solutions in production environments.
- Strong communication and stakeholder management skills.
PythonAI Developer
Primary Skill-set (Must have)
- Generative AI Expertise: 2-3 years of experience in designing and implementing generative AI solutions, including knowledge of various generative and autoregressive models. Ability to apply generative AI techniques to diverse use cases such as image generation, text generation, and creative content synthesis.
• 2 years of experience in prompt engineering, fine tuning, agentic framework, GenAI SDK’s
• 1-2 years of experience in Agentic AI frameworks like Autogen, Lanngraph, MS Agent SDK, A2A, MCP, A2P, memory concepts, multi agent orchestration
• 7+ years of experience in Python
• 5+ years of experience in software development
• Azure Proficiency: 3-5 years of experience with Azure cloud services relevant to AI, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Kubernetes Service (AKS). 2+ years of experience in Azure's capabilities to architect end-to-end AI solutions and optimize performance.
• Architecture Design: 3-5 years of skills with the ability to design scalable, reliable, and cost-effective architectures for AI solutions. Proficiency in designing distributed systems, microservices architectures, and containerized solutions using technologies such as Docker and Kubernetes.
Secondary Skills (Good to have)
• Security and Compliance: Understanding of security principles and best practices in AI development, with the ability to implement security controls, encryption mechanisms, and access management policies to protect AI models and sensitive data.
• Integration and Deployment: Proficiency in implementing CI/CD pipelines, automation scripts, and infrastructure as code (IaC) using tools such as Azure DevOps, Terraform, or Ansible. Experience in containerization and orchestration of AI workloads using Docker and Kubernetes.
• Software Development: Strong programming skills in languages such as Python, with experience in developing AI applications, RESTful APIs, and microservices architectures. Familiarity with software development methodologies such as Agile or Scrum.
• Communication and Presentation: Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders. Experience in preparing and delivering technical presentations, architecture diagrams, and documentation to communicate architectural decisions and design rationale effectively.
PythonWe are seeking Generative AI Developers with strong Python programming and AI/ML expertise to build, deploy, and optimize LLM-powered applications. The role involves developing RAG solutions, AI agents, and enterprise GenAI applications while collaborating with cross-functional teams.
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PythonRole Overview
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon