Technical Architect- AI

Role description Job Description 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.

Technical Skills: Required:

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 Professional 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=X2C2TM=1) digital experience to clients and