Now,we’reentering a new phase of growth and are looking fora Senior AI/ML Engineer to serve as PDW’s senior technical leader for enterprise AI—helping define our AI strategy, architecture, and technical roadmap while remaining hands‑on in building and scaling the capabilities that put that vision into production.

What You’ll Do

- Serve as PDW’s senior technical authority for enterprise AI/ML, partnering with technology leadership to shape the AI strategy, architecture, technical roadmap, engineering standards, and investment priorities—and taking hands‑on ownership of turning that direction into working capabilities.
- Design and implement AI orchestration and control capabilities including model routing, authentication and authorization, policy enforcement, observability, usage tracking, cost management, and auditability.
- Build production retrieval‑augmented generation (RAG) solutions, including ingestion pipelines, chunking, embeddings, vector search, retrieval, reranking, and generation across structured and unstructured enterprise data.
- Develop AI agents and agentic workflows that securely interact with enterprise applications, APIs, data sources, and business processes while respecting underlying identity and authorization boundaries.
- Partner with Engineering, Manufacturing, IT, Cybersecurity, and business teams to identify high‑value AI opportunities, rapidly prototype solutions, evaluate technical feasibility, and transition successful concepts into production.
- Develop repeatable model evaluation and benchmarking frameworks to compare models and architectures based on quality, accuracy, latency, reliability, security, and cost using PDW‑specific workloads.
- Design, build, and operate production AI/ML applications, APIs,



data pipelines, evaluation frameworks, and supporting infrastructure using modern software engineering, CI/CD, and MLOps practices.
- Evaluate and optimize AI workloads across SaaS, API, cloud, GPU, and internally hosted infrastructure, including opportunities for model optimization, fine‑tuning, quantization, and accelerated inference.
- Implement technical controls for AI security and governance, including data protection, access control, model and provider restrictions, guardrails, validation, human oversight, and protection against threats such as prompt injection and data leakage.
- Establish PDW’s AI engineering discipline, including reusable patterns, reference architectures, development standards, documentation, and technical governance; mentor engineers and teams adopting AI; and continuously evaluate emerging technologies to recommend how PDW’s AI strategy and architecture should evolve.

Requirements

- 7+ years of progressive experience in software engineering, machine learning engineering, data engineering, AI engineering, or a related technical discipline, with significant recent hands‑on experience developing AI/ML systems.
- Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Engineering, or a related STEM discipline,



or equivalent combination of education and relevant professional experience.
- Advanced proficiency in Python and demonstrated experience building production‑quality software, services, APIs, data pipelines, and distributed systems.
- Hands‑on experience with modern AI/ML frameworks such as PyTorch, TensorFlow, or JAX, with a solid understanding of machine learning fundamentals, neural networks, transformers, embeddings, and modern generative AI architectures.
- Demonstrated experience building production applications using large language models and foundation models, including prompt engineering, structured generation, tool/function calling, and model APIs.
- Demonstrated experience designing and implementing RAG architectures, including document ingestion, embeddings, vector databases or search, retrieval strategies, reranking, context management, and evaluation.
- Experience building AI agents, tool‑enabled models, workflow orchestration, or other agentic systems that interact with applications, APIs, and enterprise data.
- Experience deploying and operating AI/ML systems in production, including automated testing, CI/CD, containers, monitoring, evaluation, observability, performance optimization, and MLOps practices.
- Strong understanding of secure application architecture, identity and access control, data protection, and the security considerations associated with enterprise AI systems.
- Demonstrated ability to independently translate ambiguous business or technical problems into architectures and working solutions, troubleshoot complex systems, communicate effectively with technical and non‑technical stakeholders, and take solutions from exploration through production.
- Must have th

📌 Senior AI/ML Engineer (Canada)
🏢 PDW
📍 Canada