
We are seeking a hands-on and technically strong Generative AI Engineer to join the AI Platform Capabilities team as part of a Platform Implementation Partner engagement. In this role, you will design, build, and deploy enterprise-grade Generative AI platform capabilities across multiple Local Business Units operating on both GCP and Azure environments.
The role focuses on engineering production-ready and reusable GenAI components across the full AI stack, including Decision & Orchestration Layers, Execution Runtime Layers, and Build & Lifecycle Layers. You will work closely with implementation partners and Data & AI teams to ensure scalable, enterprise-compliant AI capabilities are delivered within project timelines. This is a deeply technical engineering role with strong emphasis on implementation and operationalization rather than client management.
Key Responsibilities:
- Design and implement enterprise-grade RAG pipelines including ingestion, chunking, embeddings, vector search, retrieval logic, and evaluation frameworks.
- Build multi-agent AI systems with orchestration, semantic routing, memory management, workflow execution, and agent communication capabilities.
- Develop centralized LLM Gateway solutions covering model routing, observability, caching, rate limiting, and policy enforcement.
- Implement scalable GenAIOps, AgentOps, and MLOps frameworks for deployment, monitoring, evaluation, and governance of AI systems.
- Build scalable AI services and REST APIs using Python and deploy them on cloud-native infrastructure.
- Implement AI governance, safety guardrails, audit logging, PII protection, and compliance frameworks.
- Integrate AI solutions into CI/CD pipelines and provision infrastructure using Infrastructure-as-Code practices.
Must-Have Skills:
- Strong hands-on experience with Generative AI, RAG pipelines, embeddings, vector databases, and retrieval evaluation.
- Experience building agentic AI systems including orchestration frameworks, semantic routers, reasoning engines, and memory management.
- Expertise with LLM Gateway implementations, tool integrations, event-driven architectures, and AI runtime systems.
- Solid understanding of GenAIOps, AgentOps, and MLOps principles and tooling.
- Strong experience with GCP AI/ML ecosystem including Vertex AI, Cloud Run, GKE, Pub/Sub, BigQuery, and Cloud Storage.
- Excellent Python programming skills for AI pipelines, APIs, and automation workflows.
- Experience implementing AI governance, safety, compliance, and monitoring frameworks.
- Hands-on experience with CI/CD pipelines and Terraform-based infrastructure automation.
Nice-to-Have Skills:
- Experience working in multi-cloud environments across GCP and Azure.
- Familiarity with LangChain, LlamaIndex, RAGAS, DeepEval, or Vertex AI Rapid Eval.
- Exposure to Knowledge Graphs, Document Intelligence platforms, and Agent Marketplace concepts.
- Experience with enterprise AI-ready data layers including Vector Stores, Feature Stores, and Embedding Infrastructure.
- BFSI domain knowledge including regulatory compliance and data sovereignty considerations.
- Google Cloud Professional Machine Learning Engineer certification.
- Experience working in large-scale enterprise transformation programs.

About Quantiphi
About
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Since its inception in 2013, Quantiphi has solved the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve accelerated and quantifiable business results.
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