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Mphasis

Technical Architect

Toronto, ON, Canada

Contract

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Job details

Job type: Contract
Apply by: Oct 24, 2026
Posting language: English
Posted: Oct 6, 2026

Original job 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.

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 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 their end customers. Mphasis’ Service Transformation approach helps ‘shrink the core’ through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis’ core reference architectures and tools, speed and innovation with domain expertise and specialization are key to building strong relationships with marquee clients.

Equal Opportunity Employer

Mphasis is an equal opportunity/affirmative action employer. We provide equal employment opportunities to applicants and existing associates and evaluate qualified candidates without regard to race, gender, national origin, ancestry, age, color, religious creed, marital status, genetic information, sexual orientation, gender identity, gender expression, sex (including pregnancy, breast feeding and related medical conditions), mental or physical disability, medical conditions military and veteran status or any other status or condition protected by applicable federal, state, or local laws, governmental regulations and executive orders. View the EEO in the law poster , view the EEO in the law supplement . To view the pay transparency nondiscrimination provision please click and to view the E-Verify posting click .

Mphasis is committed to providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of disability to search and apply for a career opportunity, please send an email to accomodationrequest@mphasis.com and let us know your contact information and the nature of your request.

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More about this job

Responsibilities

Define enterprise AI architecture standards and lead the design and delivery of scalable Generative AI, RAG, GraphRAG, and agentic solutions on Google Cloud. Establish secure, governed, production-ready AI platforms while collaborating with stakeholders, guiding engineering teams, and advising executives on architecture and investment strategies.

Requirements

Requires 10+ years of overall experience, including 5+ years designing AI/ML solutions and 3+ years delivering enterprise Generative AI and LLM applications. Requires a bachelor's degree in Computer Science or Data Science and expertise in GCP, RAG, LLMs, vector search, AI frameworks, MLOps, security, and governance; relevant Google Cloud and Databricks certifications are preferred.

Skills

Generative AI
Large Language Models
Retrieval-Augmented Generation
Agentic AI
Google Cloud Platform
Vertex AI
Gemini
Vector Databases
Python
LangChain
LangGraph
LlamaIndex
RAG Architecture
AI Governance
Cloud Architecture
MLOps

Visa sponsorship

Not detected in the job text

Keywords

Generative AI
Large Language Models
Gemini
Vertex AI
Google Cloud Platform
Retrieval-Augmented Generation
GraphRAG
Knowledge Graphs
Semantic Search
Embeddings
Vector Search
Vertex AI Vector Search
Pinecone
ChromaDB
FAISS
pgVector
LangChain
LangGraph
LlamaIndex
Python
FastAPI
BigQuery
Google Kubernetes Engine
Vertex AI Pipelines
MLflow
Terraform
Prompt Engineering
Agentic AI
Responsible AI
Google Cloud Professional Cloud Architect

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