Job Summary – AI Architect

We are seeking a highly experienced AI Architect to lead the design, architecture, and implementation of enterprise-scale Generative AI solutions on Google Cloud Platform (GCP). The ideal candidate will possess deep expertise in Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Agentic AI, Vector Databases, and the Google AI ecosystem including Vertex AI and Gemini. The role requires a blend of strategic architecture leadership and hands‑on technical expertise to deliver scalable, secure, and production‑ready AI platforms.

Years of Experience:

- 10+ years of overall experience in software engineering, cloud architecture, or data platforms.
- 5+ years of experience designing and implementing AI/ML solutions.
- 3+ years of experience delivering Generative AI and LLM-based applications in enterprise environments.
- Proven experience implementing RAG architectures, conversational AI platforms, and AI-powered knowledge management solutions.

Required

Technical Skills:

- Bachelor’s degree in Computer Science, Data Science.
- Generative AI & LLMs: Gemini, Vertex AI, OpenAI, Llama, Foundation Models, Prompt Engineering, Conversational AI, Agentic AI / Multi-Agent Architectures, AI Model Evaluation and Monitoring
- RAG & Knowledge Systems: Retrieval-Augmented Generation (RAG), GraphRAG, Knowledge Graphs, Semantic Search, Embeddings and Vector Search, Document Intelligence and Enterprise Search
- Google Cloud Platform (GCP): Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Cloud SQL, Pub/Sub, Dataflow, GKE (Google Kubernetes Engine)
- Vector Databases & Search: Vertex AI Vector Search, Pinecone,



ChromaDB, FAISS, pgVector
- AI Frameworks & Development: LangChain, LangGraph, LlamaIndex, FastAPI, REST APIs, Python, SQL
- MLOps & DevOps: Vertex AI Pipelines, MLflow, CI/CD for AI Applications, Model Governance & Monitoring, Infrastructure as Code (Terraform)
- Security & Governance: Responsible AI, AI Risk Management, Data Governance, Security Architecture, Compliance & Audit Controls

Preferred Certifications

- Google Cloud Professional Cloud Architect
- Google Cloud Skilled Machine Learning Engineer
- Google Generative AI Certifications
- Databricks Generative AI Certifications (preferred)

Key Responsibilities

AI Architecture & Strategy

- Define enterprise AI architecture standards, patterns, and best practices.
- Design end-to-end Generative AI, RAG, and Agentic AI solutions.
- Develop AI roadmaps aligned with business objectives and technology strategy.

RAG & Knowledge Platform Design

- Architect large-scale RAG and GraphRAG solutions.
- Design document ingestion, chunking, indexing, retrieval, re-ranking, and grounding strategies.
- Optimize AI solution accuracy, scalability, latency, and cost efficiency.
- Build enterprise knowledge platforms leveraging structured and unstructured data sources.

Generative AI Solution Delivery





- Lead the design and implementation of AI assistants, chatbots, copilots, and autonomous agents.
- Enable integration of LLMs with enterprise systems, APIs, and workflows.
- Establish frameworks for prompt engineering, model evaluation, and continuous improvement.

Cloud & Platform Engineering

- Architect scalable AI platforms using GCP services.
- Drive cloud-native AI application development and deployment.
- Define best practices for performance optimization, reliability, observability, and resilience.

Governance, Security & Responsible AI

- Implement AI governance frameworks, security controls, and monitoring capabilities.
- Ensure compliance with enterprise policies, data privacy, and regulatory requirements.
- Establish standards for model transparency, explainability, and risk management.

Leadership & Collaboration

- Partner with business stakeholders, product owners, data engineers, and AI teams.
- Conduct architecture reviews and technical design workshops.
- Mentor engineering teams and promote AI adoption across the organization.
- Present architecture recommendations and investment strategies to executive leadership.

Location:

Toronto, ON

Work Mode:

Hybrid, 3-4 days per week

About Mphasis

Mphasis applies next-generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis’ Front2Back™ Transformation approach. Front2Back™ uses the exponential power of cloud and cognitive to provide hyper-personalized (C=X

📌 Technical Architect- AI (Canada)
🏢 Mphasis
📍 Canada