Machine Learning Developer

Machine Learning Developer

Lachine, QC (Hybrid)

Schedule: M-F, Professional Working Day

Contract: 6 months

Introduction

Our client is seeking an experienced Machine Learning Developer to design, build, deploy, and maintain end to end ML solutions that power data driven decision making across our digital ecosystem. This role is ideal for someone who thrives at the intersection of applied machine learning, ML Ops engineering, and production-grade software development.

You will work closely with cross functional teams—including data engineers, software developers, product owners, and project leaders—to transform ambiguous real world data and business problems into scalable, resilient, and high impact ML systems.

Responsibilities

1. End to End Machine Learning Development

• Build and own ML solutions from data ingestion through modelling, evaluation, deployment, and monitoring.

• Develop, train, and evaluate machine learning models using modern ML frameworks and libraries.

2. Production Engineering & MLOps

• Deploy, operationalize, and maintain ML models in production environments, implementing CI/CD pipelines, Docker/containerization, orchestration, automated retraining, and monitoring.

• Write modular, production ready Python code and reusable ML components.

3. Data Preparation & Feature Engineering

• Extract, clean, transform, and validate datasets from diverse sources to support robust model development.

• Handle ambiguity in real world, imperfect data and design reproducible data processing pipelines.

4. Model Quality & Risk Management

• Apply rigorous evaluation practices: cross validation, bias/variance analysis, overfitting detection, and data leakage prevention.

• Monitor models for drift, performance degradation, and operational issues.

5. Collaboration & Stakeholder Engagement

• Work cross functionally with engineers, developers, architects, and project teams to align technical solutions with business objectives.

• Clearly communicate findings, risks, solution design, and technical trade offs to both technical and non technical stakeholders.

6. Innovation & Modern ML

• Work with emerging approaches such as LLMs, SLMs, embeddings, and prompt based workflows.

• Stay up to date with current ML engineering, MLOps practices, tooling, and cloud native capabilities.

Required Qualifications, Experience & Skills

• 5+ years of experience designing and implementing end to end ML solutions in production.

• Strong command of ML algorithms, model development, training, validation, and optimization.

• Expertise in Python, ML libraries, and version control (Git).

• Clear understanding of model evaluation, data leakage, and the bias/variance trade off.

• Hands on experience with cloud platforms (AWS/Azure/GCP) and MLOps practices, including Docker, CI/CD, deployment, and monitoring.

• Demonstrated success deploying and maintaining production ML models and writing modular, production grade code.

• Strong experience preparing, transforming, and validating complex real world datasets (in Snowflake or similar cloud data platforms).

• Experience with enterprise system data (SAP, Salesforce, PLM, Teamcenter) is desirable.

• Familiarity with LLMs/SLMs and modern ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace).

• Excellent problem solving abilities and communication skills.

• Proven ability to work cross functionally with engineering and product teams.

A Snapshot of a Typical Day

• Reviewing model performance dashboards to detect drift or anomalies.

• Working with engineers to refine a data pipeline or debug a production model issue.

• Pair programming with developers to implement new pipeline components or optimize code for production.

• Running experiments on new ML architectures or tuning hyperparameters for an active use case.

• Meeting with project teams to translate business needs into ML ready requirements, and effectively communicate solution design to build confidence, validate outcomes and drive adoption

• Evaluating risks such as data leakage, insufficient sampling, data imbalance, or other data quality issues and proposing mitigations.

• Exploring and testing improvements using LLM based workflows or modern ML tooling.

This role offers the opportunity to make meaningful impact by delivering scalable, stable, and business critical intelligent systems.

What We Offer

Why work with Brunel? We are proud to offer exciting career opportunities from over 100 offices globally in 42 countries. Advancing your career takes time and effort – let us match you to your ideal position.

About Us

Brunel has a reputation for working with some of the best in the business. That’s what we continually strive for. Brunel provides the global recruitment and workforce services you need to lead your industry. With 50 years of market experience in Renewable Energy, Automotive, Oil & Gas, Life Sciences, Mining and Infrastructure, we help you finish major projects safely, compliantly, on-time, within budget and at the highest quality, so you can keep growing – anywhere in the world.