AI Research Engineer

Job Type: Permanent Work Model: Hybrid Reference code: 130069 Primary Location: Toronto, ON All Available Locations: Toronto, ON; Brossard, QC; Burlington, ON; Calgary, AB; Edmonton, AB; Fredericton, NB; Halifax, NS; Kitchener, ON; Laval, QC; Moncton, NB; Montreal, QC; Ottawa, ON; Quebec City, QC; Regina, SK; Saint John, NB; Saskatoon, SK; St. John's, NL; Vancouver, BC; Victoria, BC; Winnipeg, MB Our Purpose At Deloitte, our Purpose is to make an impact that matters. We exist to inspire and help our people, organizations, communities, and countries to thrive by building a better future. Our work underpins a prosperous society where people can find meaning and opportunity. It builds consumer and business confidence, empowers organizations to find imaginative ways of deploying capital, enables fair, trusted, and functioning social and economic institutions, and allows our friends, families, and communities to enjoy the quality of life that comes with a sustainable future. And as the largest 100% Canadian-owned and operated professional services firm in our country, we are proud to work alongside our clients to make a positive impact for all Canadians. By living our Purpose, we will make an impact that matters. • Have many careers in one Firm. • Enjoy flexible, proactive, and practical benefits that foster a culture of well-being and connectedness. • Learn from deep subject matter experts through mentoring and on the job coaching -- We are looking for a passionate AI Research Engineer to join our team. You will work at the intersection of cutting-edge AI research and product engineering—designing, evaluating, and deploying generative AI (GenAI) systems that are both reliable and impactful. This role blends fundamental research, model evaluation, and practical software engineering to push forward the next generation of intelligent applications. What will your typical day look like? Responsibilities Collaborate with product managers, engineers, and stakeholders to design AI-driven solutions that meet technical and business requirements. Research, prototype, and develop generative AI applications by combining non-deterministic LLMs with deterministic software engineering techniques. Build evaluation frameworks and benchmarks to measure model quality, reliability, and business impact. Generate regular reports on model accuracy, drift, and performance. Debug, optimize, and enhance GenAI applications using prompt engineering, reinforcement learning, fine-tuning, and software engineering best practices. Train and fine-tune large language models using Hugging Face Transformers. Apply reinforcement learning fine-tuning techniques using Hugging Face TRL (Transformers Reinforcement Learning). Manage training workflows with experiment tracking tools and distributed training accelerators (DeepSpeed, Accelerate, FSDP). Run and optimize multi-GPU training and inference, leveraging vLLM for high-throughput, low-latency serving. Contribute to the design of scalable MLOps/DevOps pipelines for model deployment, monitoring, and continuous training. Ensure compliance with data privacy, security, and responsible AI guidelines when handling training or test datasets. Stay current with emerging research in LLMs, RLHF/RLAIF, multimodal AI, and generative models; apply findings to improve our systems. Author technical documentation and contribute to publications, patents, or open-source projects where applicable. About the team Deloitte AI and Data, Deloitte's Artificial Intelligence (Al) practice is comprised of Al/ML experts with hands-on experience in developing and deploying Al/ML solutions to create competitive advantage for the Canadian businesses as part of their overall Data and Al/ML transformations journey. AI and Data Data Science team works together with Canadian businesses to envision and craft the solutions that drive automation, optimization, efficiency and many cases new opportunities with being mindful of driving responsible and transparent Al. We strive for empowering our clients' organization to become data and insight driven organizations with Al/ML first mindset to produce tangible business outcomes. Enough about us, let’s talk about you You are someone with these required skills, experience and qualifications: • 3+ years experience in machine learning engineering, data engineering, or applied research (industry or academic). • Strong programming skills in Python and experience with frameworks such as PyTorch, TensorFlow, JAX. • Hands-on experience with Hugging Face Transformers for pretraining, fine-tuning, or inference. • Experience with Hugging Face TRL for reinforcement learning fine-tuning (e.g., PPO, DPO, GRPO, RLAIF). • Practical experience managing multi-GPU training and distributed training at scale using DeepSpeed, Accelerate, or FSDP. • Experience running inference on large models using vLLM or similar optimized serving frameworks. • Familiarity with experiment tracking and reproducibility tools (e.g., W&B, MLflow). • Knowledge of MLOps practices including continuous training, continuous monitoring, and model lifecycle management. • Experience with GenAI frameworks such as LangChain, AutoGen (A2A), or MCP. • Demonstrated ability to write clean, maintainable, production-ready code. • Experience building or supporting cloud-based AI systems (GCP, AWS, or Azure; certifications preferred). • Strong grasp of reinforcement learning, NLP, and/or generative modeling (transformers, diffusion, RAG, etc.). • Track record of research contributions (papers, patents, open-source projects) is a plus. Preferred Tech Stack Candidates with experience in the following tools and frameworks will be strongly preferred: • Transformers (Hugging Face) for model training, fine-tuning, and inference • TRL (Transformers Reinforcement Learning) for RL-based fine-tuning (PPO, DPO, GRPO, RLAIF) • DeepSpeed, Accelerate, or FSDP for multi-GPU and distributed training • vLLM for optimi