Job Summary

We are looking for an experienced AI Architect to design and lead the development of an enterprise AI platform. The ideal candidate will have strong hands-on experience in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), knowledge graphs, semantic layers, and cloud-native architecture.

The role involves defining end-to-end AI architecture, building scalable and secure AI solutions, leading AI engineering teams, and integrating enterprise data platforms to deliver accurate, explainable, and governed AI-driven insights.

Key Responsibilities

- Design end-to-end AI architectures using LLMs, RAG, vector databases, and agentic AI frameworks.
- Develop enterprise ontology and knowledge graphs to connect applications, infrastructure, cost centres, owners, SLAs, and compliance policies.
- Define technology strategy for LLM providers, vector stores, graph databases, and orchestration frameworks.
- Design hybrid retrieval solutions combining vector search, knowledge graphs, and semantic-layer queries.
- Establish reusable AI platforms, APIs, prompt engineering standards, and agent orchestration patterns.
- Lead MLOps and GenAIOps practices, including model lifecycle management, evaluation, monitoring, and cost optimization.
- Integrate cloud billing, usage, monitoring, and policy data from AWS, GCP, and enterprise systems.
- Ensure data quality, lineage, entity resolution, security, privacy, and responsible AI governance.
- Collaborate with Cloud, Data Engineering, Finance, Security, DevOps, and UI teams.
- Lead technical reviews, mentor engineers, define roadmaps, and manage architecture risks.

Required Technical Skills

- Generative AI: LLMs, AWS Bedrock, Google Vertex AI, open-source foundation models.
- RAG & Vector Databases: LangChain, LlamaIndex, embeddings, chunking, hybrid search, reranking, Pinecone, Weaviate, Milvus, or pgvector.
- AI Frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.




- Knowledge Graphs & Ontology: Neo4j, Amazon Neptune, RDF/OWL, SPARQL, or Cypher.
- Cloud & Infrastructure: AWS/GCP, Kubernetes, Docker, and distributed cloud-native architectures.
- Semantic Layer & Data Engineering: Solid SQL, data modelling, dbt Semantic Layer, Cube, or similar.
- MLOps/GenAIOps: CI/CD, model and prompt versioning, evaluation frameworks, observability, and model registries.
- Programming: Strong Python; working knowledge of Java, Scala, TypeScript, or Go.
- Architecture: APIs, microservices, event-driven systems, and streaming architectures.
- Governance: Enterprise security, role-based access control, data privacy, compliance, and responsible AI.

Required Experience & Qualifications

- 912 years of overall experience in software engineering, platform engineering, data engineering, or enterprise architecture.
- At least 3–5 years of hands-on experience architecting or delivering production AI/ML/GenAI solutions.
- Proven experience leading at least 2–3 end-to-end AI/ML project lifecycles, from requirements and architecture to deployment and production support.
- Experience in enterprise-scale AI platform design, cloud integration, and cross-functional technical leadership.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

Success Metrics – First 12 Months

- Finalize the target architecture and ontology v1 within 30 days.
- Launch an MVP natural-language FinOps assistant for pilot users within 3 months.
- Establish measurable AI evaluation frameworks for accuracy, groundedness, latency, and cost.
- Roll out the solution across priority application portfolios with documented security and governance standards.

Preferred Candidate Profile

A hands-on AI Architect with strong GenAI/RAG expertise, knowledge graph and ontology experience, cloud architecture skills, and a proven track record of delivering production-grade enterprise AI solutions. Experience with FinOps, cloud cost management, and multi-cloud billing data is highly desirable.

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