About Quantiphi:

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

- 21 Google Cloud Partner of the Year awards in the past 10 years
- 3 AWS AI/ML Partner of the Year awards
- 3 NVIDIA Partner of the Year awards
- 3 Snowflake Partner of the Year awards
- Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. We are also proud to be certified as a Great Place to Work —reflecting our commitment to our people and our culture.

For more details, visit: Website or LinkedIn Page

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Required Skills:

- 8+ years of experience in Machine Learning, AI, Software Engineering, Data Science, or Solution Architecture.
- Strong experience designing and implementing enterprise-scale AI/ML architectures.
- 3+ years of hands-on experience with Generative AI, LLMs, and production AI applications.
- Solid expertise in RAG (Retrieval-Augmented Generation), embeddings, vector databases,



semantic/hybrid search, and knowledge retrieval.
- Hands-on experience with Agentic AI, AI agents, tool/function calling, orchestration, and multi-agent workflows.
- Strong proficiency in Python and experience with ML/AI frameworks such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
- Experience with LLM platforms/models such as Azure OpenAI, OpenAI, AWS Bedrock, Anthropic, Google Gemini, Llama, or equivalent.
- Strong knowledge of prompt engineering, context engineering, fine-tuning, model evaluation, guardrails, and hallucination mitigation.
- Experience designing and implementing MLOps/LLMOps pipelines, including model deployment, monitoring, evaluation, versioning, and CI/CD.
- Strong experience with at least one major cloud platform: Microsoft Azure, AWS, or GCP.
- Experience with cloud AI/ML services such as Azure Machine Learning, Azure AI Foundry, AWS SageMaker/Bedrock, or Google Vertex AI.
- Experience with Docker, Kubernetes, APIs, microservices, CI/CD, and Infrastructure as Code.
- Strong understanding of data pipelines, data lakes/lakehouses, databases, data governance, and enterprise integration.
- Experience with AI/ML security, privacy, Responsible AI, governance, and compliance.
- Strong system-design and architecture skills, with the ability to translate business requirements into scalable technical solutions.
- Excellent communication, presentation, stakeholder-management, and technical leadership skills.

Key Responsibilities:





- Design end-to-end enterprise AI/ML architectures from data ingestion through model development, deployment, inference, monitoring, and optimization.
- Lead the architecture and implementation of Generative AI and LLM-based solutions across enterprise use cases.
- Design scalable RAG architectures, including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and LLM generation.
- Architect and implement Agentic AI solutions, including AI agents, tool calling, workflow orchestration, memory, and human-in-the-loop capabilities.
- Evaluate and recommend LLMs, AI platforms, frameworks, and technologies based on performance, scalability, security, latency, and cost.
- Define architecture standards and reusable patterns for GenAI, ML, MLOps, and LLMOps.
- Lead the transition of AI/ML POCs and prototypes into production-grade solutions.
- Design and implement MLOps/LLMOps capabilities for model lifecycle management, deployment, monitoring, evaluation, and continuous improvement.
- Establish LLM evaluation and observability frameworks covering accuracy, relevance, hallucination, latency, token usage, cost, and reliability.
- Work closely with Data Scientists, ML Engineers, Software Engineers, Data Engineers, Product Managers, Security teams, and business stakeholders.
- Provide technical leadership and mentorship to AI/ML engineering teams.
- Ensure AI solutions meet enterprise requirements for security, privacy, governance, Responsible AI, and regulatory compliance.
- Identify opportunities to improve existing AI/ML platforms and optimize performance, scalability, and cloud costs.
- Create architecture diagrams, technical specifications, design documents, standards, and implementation roadmaps.
- Stay current with emerging GenAI, LLM, Agentic AI, ML, and cloud technologies and assess their applicability to business needs.

📌 Architect - Machine Learning (Canada)
🏢 Quantiphi
📍 Canada