Senior Machine Learning Engineer

Wiser Solutions, Inc.

. Take customer problems from idea to working prototype quickly .

Posted 11/5/2026full-timeRemote • Canada, United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in the full ML lifecycle, including model training, evaluation, and deployment on AWS. Proficient in building production applications with LLMs and implementing MLOps practices to ensure model performance and reliability.

Highest-signal resume keywords

ML EngineeringModel Training And EvaluationAWS For ML WorkloadsMLOps PracticesPython Programming

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningData ScienceModel VersioningExperiment DesignFeature EngineeringLLM Fine-TuningA/B TestingPrecision And RecallEntity ResolutionSimilarity Search

Soft Skills

Curiosity About AI Tools

Tools & Technologies

AWS SageMakerMLflowWeights & BiasesCursorClaude CodeGitHub CopilotAirflow

Industry Keywords

RetailE-CommerceProduct Data

Tech Stack

Tools & technologies

AirflowAWSPython

About the role

Key responsibilities & impact

Take customer problems from idea to working prototype quickly
Prove or discard approaches with data and move successful solutions into production
Train, fine-tune, and evaluate classical ML, embedding, and LLM models for matching, classification, and extraction
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 evaluations using labeled golden sets, precision, recall, LLM-as-judge, shadow testing, and A/B testing
Monitor production models for drift, output quality, latency, and cost per prediction
Fix issues identified through production monitoring

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, choosing metrics, handling 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 engineering practices including version control, testing, CI/CD, and code review
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
Preferred: Experience optimizing 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 like Airflow (helpful, not required)

Benefits

Comp & perks

Performance-based discretionary bonuses
Variable pay plans available for some positions
Remote work arrangement
Reasonable accommodations for applicants with disabilities