Job Summary

We are seeking a Gen AI Consultant with 4-6 years of relevant experience to design and deliver enterprise GenAI solutions using knowledge graphs, retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI across GCP and Azure. The role will combine hands-on solution architecture, consulting, stakeholder engagement, and delivery leadership for scalable, secure, and production-ready AI applications.

Key Responsibilities

- AI Solution Development: Lead the design and delivery of GenAI solutions using LLMs, knowledge graph-based RAG, semantic retrieval, and agentic workflows to address real-world business problems.
- Knowledge Graph and RAG Architecture: Design graph-enhanced retrieval pipelines, entity and relationship models, ontology-driven context, hybrid search, grounding, citation, and evaluation approaches for accurate enterprise responses.
- Consulting and Solution Architecture: Translate business requirements into solution blueprints, define target architecture and delivery roadmaps, and guide stakeholders through use-case prioritization, feasibility, and value realization.
- Cloud-Based Deployment: Architect, deploy, and optimize GenAI solutions on GCP Vertex AI and Azure OpenAI/Azure AI services, ensuring scalability, reliability, security, and performance.
- Backend, Integration, and Orchestration: Develop and govern Python-based services, REST APIs, microservices, model orchestration, tool integrations, and cloud-native data connections for enterprise AI applications.
- LLM Engineering and Evaluation: Lead prompt engineering, model selection, experimentation, guardrails, responsible AI controls, and evaluation of retrieval quality, groundedness, latency, cost, and throughput.




- Delivery and Stakeholder Leadership: Own technical delivery, risks, dependencies, quality reviews, client communication, and cross-functional collaboration across data, engineering, product, and business teams.
- Team Leadership and Knowledge Sharing: Mentor consultants and engineers, establish reusable accelerators and engineering standards, and maintain clear solution, architecture, and operational documentation.

Required Qualification
- 4-6 years of relevant experience in AI/ML, data science, or AI solution consulting, including hands-on experience with Generative AI / LLM systems.
- Proven expertise in knowledge graph-based RAG, LLM orchestration, LangChain or comparable frameworks, GCP Vertex AI, Azure OpenAI, and Azure AI services.
- Robust Python programming skills with experience building REST APIs, microservices, retrieval pipelines, and production-grade AI services.
- Deep knowledge of knowledge graphs, ontologies, entity resolution, graph databases, vector search, hybrid retrieval, agent-based systems, prompt engineering, and model evaluation.

Disclaimer: The following serves as an informative reference for the tasks you may be required to perform. However, it does not constitute an integral component of your employment agreement and is subject to periodic modifications to align with evolving circumstances . Please Note : We appreciate the accuracy and authenticity of the information you provide, as it plays a key role in your candidacy. As part of the Background Verification Process, we may verify your employment, education, and other details. Please ensure all information is factual and submitted on time. For any assistance, your recruiter is available to support you.

📌 Consultant-Knowledge Graph RAG (Bengaluru)
🏢 Evalueserve
📍 Bengaluru