Senior Machine Learning Engineer

Wiser Solutions, Inc.

. Take customer problems from idea to working prototype quickly .

Posted 11/5/2026full-timeRemote • CanadaSenior💰 CA$160,000 - CA$200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in the full ML lifecycle, including model training, evaluation, and deployment on AWS. Proficient in building customer-facing AI features and applying MLOps practices to ensure production readiness and performance monitoring.

Highest-signal resume keywords

Machine Learning EngineeringModel Training And EvaluationAWS For ML WorkloadsMLOps PracticesPython Programming

ATS Keywords

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Hard Skills

Machine LearningModel EvaluationFeature EngineeringExperiment DesignLLM Fine-TuningEmbedding ModelsProduction ApplicationsData ScienceMetric SelectionClass Imbalance

Soft Skills

Curiosity About AI ToolsRapid Prototyping

Tools & Technologies

AWS SageMakerMLflowWeights & BiasesCursorClaude CodeGitHub CopilotSageMaker PipelinesECS/EKSS3Airflow

Industry Keywords

RetailE-CommerceProduct DataEntity ResolutionSimilarity Search

Tech Stack

Tools & technologies

AirflowAWSPython

About the role

Key responsibilities & impact

Take customer problems from idea to working prototype quickly
Prove or disprove approaches with data and move successful solutions into production
Train, fine-tune, and evaluate models for matching, classification, and extraction problems
Select appropriate approaches across classical ML, embedding models, and LLMs based on quality and cost
Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS
Own the full ML lifecycle, including experiment tracking, model versioning, deployment, monitoring, and retraining
Build evaluation systems using labeled golden sets, offline precision and recall metrics, LLM-as-judge, and shadow or A/B testing
Monitor production models for drift, output quality, latency, and cost per prediction
Fix issues identified through production monitoring
Build customer-facing AI features for product matching, classification, and attribute extraction at large scale

Requirements

What you’ll need

7+ years in ML engineering, applied data science, or AI engineering
Track record of shipping ML or AI systems to production and owning them after launch
Hands-on experience training and evaluating models, including feature engineering, experiment design, metric selection, class imbalance, and noisy labels
Hands-on experience building production applications with LLMs, including RAG, embeddings, and vector databases
Working knowledge of MLOps practices and tooling such as MLflow, SageMaker Pipelines, Weights & Biases, or similar
Experience with AWS for ML workloads, including SageMaker, Bedrock, Lambda, ECS/EKS, and S3
Strong Python and solid engineering habits, including version control, testing, CI/CD, and code review
Habit of building quickly to learn and testing ideas rapidly
Daily use of AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar
Curiosity about new AI tools
Preferred: Experience with LLM fine-tuning and choosing between fine-tuning, prompt engineering, and smaller task-specific models
Preferred: Experience reducing inference cost through distillation, quantization, caching, or tiered model routing
Preferred: Background in retail, e-commerce, or product data
Preferred: Experience with entity resolution or similarity search at scale
Preferred: Comfort with data pipelines and orchestration tools such as Airflow
Ability to perform the stated physical demands, including prolonged sitting and occasionally lifting or moving up to 25 pounds

Benefits

Comp & perks

Performance-based discretionary bonuses and variable pay plans are available for some positions
Remote work availability
Reasonable accommodations during the application process