Senior Consultant, ML/AI Engineer, Data & AI at KPMG India

Vancouver, British Columbia, Canada -

Full Time

Start Date

Immediate

Expiry Date

07 Jul, 26

Salary

0.0

Posted On

08 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Artificial Intelligence, Generative AI, MLOps, Azure, TensorFlow, PyTorch, Databricks, Cloud Computing, Data Engineering, Model Deployment, CI/CD, Consulting, Communication, Problem Solving

Industry

Financial Services

Description

Overview At KPMG, you’ll join a team of diverse and dedicated problem solvers, connected by a common cause: turning insight into opportunity for clients and communities around the world. Are you a technically strong and business‑oriented Machine Learning / AI Engineer with a passion for building and scaling intelligent solutions? Our team is looking for a hands‑on engineer with deep experience in AI/ML engineering and AI/ML engineering operations who can partner with clients to design, build, and operationalize AI‑powered solutions at scale. This role will focus on translating advanced analytics, machine learning, and generative AI use cases into secure, scalable, and production‑ready solutions across on-prem and cloud environments (ideally on Azure but also GCP and AWS). What you will do Partner with clients to understand business problems and identify opportunities to apply AI and advanced analytics solutions. Translate business and analytical requirements into end‑to‑end ML/AI solution design, Execute ML/AI engineering tasks including exploratory data analysis, data preparation, model development (e.g., forecasting, classification, recommendation, anomaly detection) using tech stack such as Python and common ML frameworks (e.g., scikit‑learn, TensorFlow, PyTorch, Azure ML Studio, Databricks MLFlow). Develop and optimize AI and GenAI solutions using state-of-the-art tools and platform (AI Foundry, GCP Vertex AI, AWS Sagemaker and Bedrock). Operationalize AI/ML pipelines using AI/ML Ops best practices, including model deployment versioning, CI/CD, automated testing, and monitoring. Implement model monitoring, performance tuning, drift detection, and retraining strategies in production environments. Collaborate with data engineers to ensure reliable, scalable data pipelines that support model training and inference. Apply responsible AI principles, including explainability, bias detection, model governance, and compliance with security and privacy standards. Support client workshops, technical discussions, and stakeholder presentations related to AI strategy, solution design, and implementation. What you bring to the role University degree in computer science, engineering, data science, mathematics, or a related discipline. 3+ years of professional experience in machine learning, data science, AI engineering, or a related field, with demonstrated experience delivering production ML solutions. Strong proficiency in Python for data analysis, machine learning, and model development. Hands‑on experience with machine learning frameworks/libraries and platform tools (e.g., scikit‑learn, TensorFlow, PyTorch, Azure ML Studio, Databricks MLFlow). Solid understanding of ML algorithms, statistics, model evaluation techniques, and feature engineering. Experience designing and implementing end‑to‑end ML pipelines, including data preprocessing, model training, validation, deployment, and monitoring. Practical experience with ML Ops practices, including CI/CD, model versioning, experiment tracking, and automated retraining. Experience deploying ML models to cloud environments (Azure, AWS, or GCP) with an understanding of cloud‑native architecture and security principles. Familiarity with big data or distributed processing frameworks (e.g., Spark) is an asset. Experience with generative AI, large language models (LLMs), prompt engineering, or retrieval‑augmented generation (RAG) is essential, experience with fine-tuning foundational models is an asset. Strong consulting and communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders. Proven ability to collaborate within cross‑functional and multi‑disciplinary teams to solve complex business problems. Certifications (Preferred) Cloud AI / ML certifications (e.g., Azure AI Engineer Associate or better, AWS Machine Learning Specialty or better, Google Professional ML Engineer or better, Databricks ML Engineer Associate or better, Databricks Generative AI Engineer). KPMG Ontario Region Pay Range Information The expected base salary range for this position is $77,000 to $102,000 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills & competencies, and unique qualifications. In addition, KPMG offers a comprehensive and competitive Total Rewards program. KPMG BC Region Pay Range Information The expected base salary range for this position is $73,000 to $100,000 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills & competencies, and unique qualifications. In addition, KPMG offers a comprehensive and competitive Total Rewards program. Providing you with the support you need to be at your best Our Values, The KPMG Way Integrity, we do what is right | Excellence, we never stop learning and improving | Courage, we think and act boldly | Together, we respect each other and draw strength from our differences | For Better, we do what matters KPMG in Canada is a proud equal opportunities employer and we are committed to creating a respectful, inclusive and barrier-free workplace that allows all of our people to reach their full potential. A diverse workforce is key to our success and we believe in bringing your whole self to work. We welcome all qualified candidates to apply and hope you will choose KPMG in Canada as your employer of choice. Adjustments and accommodations throughout the recruitment process At KPMG, we are committed to fostering an inclusive recruitment process where all candidates can be themselves and excel. We aim to provide a positive experience and are prepared to offer adjustments or accommodations to help you perform at your best. Adjustments (informal requests), such as extra preparation time or the option for micro breaks during interviews, and accommodations (formal requests), such as accessible communication supports or technology aids, are tailored to individual needs and role requirements. You will have an opportunity to request an adjustment or accommodation at any point throughout the recruitment process. If you require support, please contact KPMG’s Employee Relations Service team by calling 1-888-466-4778. AI Usage We embrace the use of artificial intelligence (AI) to enhance the candidate experience and streamline our recruitment processes. AI tools may help with organizing applications or surfacing relevant qualifications. However, no hiring decisions are made using AI. Every hiring decision is made by our hiring managers and recruitment professionals, who are equipped with training that empowers them to use these tools responsibly. AI technologies used in our recruitment process undergo detailed risk assessments, including security and privacy requirements, that align with KPMG’s Trusted AI framework. We believe technology should empower human judgment, not replace it. It’s one of the many ways we’re delivering on our vision of being a technology-first, people-driven firm.

Responsibilities

The role involves partnering with clients to design, build, and operationalize scalable AI and machine learning solutions. You will be responsible for developing end-to-end ML pipelines, implementing MLOps best practices, and providing strategic technical guidance to stakeholders.