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Quantiphi

ML Architect

Canada

Full-time

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Job details

Job type: Full-time
Experience: 8+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
Posting language: English
Posted: Oct 8, 2026

Original job description

What you will do:

As an Architect, Machine Learning Engineer at Quantiphi, you will be responsible for designing and developing advanced machine learning models and algorithms to solve complex business problems. You will work on optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.

Basic Qualifications (BQ):

8+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
Good Experience developing applications using LLMs with Langchain.
Must have experience using GenAI frameworks such as vertexAI, OpenAI, AWS Bedrock.
Must have Hands-on experience fine-tuning large language models( LLM) and Generative AI (GAI), specifically LLama2.
Must have Hands-on experience working with (Retrieval Augmented Generation) RAG architecture and experience using vector indexing such as Opensearch, Elasticsearch.
Strong familiarity with higher-level trends in LLMs and open-source platforms.
Should have experience with Deep Learning Concepts. Transformers, BERT, Attention models
Prompt Engineering: Engineer prompts and optimize few-shot techniques to enhance LLM's performance on specific tasks, e.g. personalized recommendations.
Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
Response Quality: Collaborate with ML and Integration engineers to leverage LLM's pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.
Implement and manage MLOps principles and best practices for Gen AI models
Thorough understanding of NLP techniques for text representation and modeling
Able to effectively design software architecture as required
Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc.
Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.

What is in it for you:

Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale.
Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges.
Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents.
Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.

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More about this job

Responsibilities

Design, develop, optimize, and deploy machine learning models and generative AI solutions on AWS, including end-to-end architectures for training, inference, and retraining. Apply prompt engineering, model evaluation, and MLOps practices while collaborating with cross-functional teams to deliver reliable, scalable solutions.

Requirements

Requires 8+ years of hands-on experience implementing cloud machine learning solutions on AWS, including SageMaker, and practical experience with LLMs, generative AI frameworks, Llama 2 fine-tuning, and RAG with vector indexing. Candidates should also have knowledge of deep learning, NLP, machine learning methods, workflow orchestration, software architecture, and cross-functional collaboration.

Skills

AWS Machine Learning
Amazon SageMaker
Large Language Models
Generative AI
LangChain
AWS Bedrock
Vertex AI
OpenAI
LLM Fine-Tuning
Retrieval-Augmented Generation
OpenSearch
Elasticsearch
MLOps
Natural Language Processing
Deep Learning
Workflow Orchestration

Visa sponsorship

Not detected in the job text

Keywords

AWS
Amazon SageMaker
AWS Lambda
AWS Bedrock
Vertex AI
OpenAI
LangChain
Large Language Models
Generative AI
Llama 2
Retrieval-Augmented Generation
OpenSearch
Elasticsearch
MLOps
Natural Language Processing
Deep Learning
Transformers
BERT
Attention Models
Prompt Engineering
Model Evaluation
Few-Shot Learning
Supervised Learning
Unsupervised Learning
Airflow
Step Functions
SageMaker Pipelines
Kubeflow
Model Training
Real-Time Inference

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