Senior ML Engineer - End-to-End AI Platform (Remote)

Wiser Solutions; United States; Remote; USD 114,000 - 143,000; Full time

Job summary

Wiser Solutions seeks a Senior ML Engineer to build and deploy our customer-facing AI platform. You will own end-to-end ML systems from idea to production, tackling matching, classification, and attribute extraction across large-scale data from global retailers and marketplaces.

Emphasis on fast prototyping, cost-aware modeling, and hands-on experimentation. You will work with embeddings, RAG, and vector databases on AWS, applying MLflow, SageMaker Pipelines, and Weights & Biases to monitor,

Qualifications

• 7+ years in ML engineering, applied data science, or AI engineering.
• A track record of shipping ML or AI systems to production and owning them after launch.
• Hands-on experience training and evaluating models: feature engineering, experiment design, metrics, handling imbalance.
• Hands-on experience building production apps 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 (SageMaker, Bedrock, Lambda, ECS/EKS, S3).
• Strong Python and solid engineering habits: version control, testing, CI/CD, and code review.
• A habit of building quickly to learn: test an idea in two days rather than two weeks.
• Daily use of AI coding assistants and curiosity about new AI tools.

Responsibilities

• Take customer problems from idea to working prototype quickly, prove or kill an approach with data, and move what works into production
• Train, fine-tune, and evaluate models for matching, classification, and extraction problems.
• Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS
• Own the full ML lifecycle: experiment tracking, model versioning, deployment, monitoring, and retraining
• Build evaluation that tells us whether something works: labeled golden sets, offline metrics, LLM-as-judge, and shadow or A/B testing before rollout
• Monitor models in production for drift, output quality, latency, and cost per prediction, and fix what you find

Skills

ML engineering

Production ML

Python

Experimentation

MLOps

LLMs

RAG systems

Embeddings

Model evaluation

Retrieval

Tools

MLflow

SageMaker Pipelines

Weights & Biases

Airflow

SageMaker

AWS

Job description

Wiser Solutions seeks a Senior ML Engineer to build and deploy our customer-facing AI platform. You will own end-to-end ML systems from idea to production, tackling matching, classification, and attribute extraction across large-scale data from global retailers and marketplaces.

Emphasis on fast prototyping, cost-aware modeling, and hands-on experimentation. You will work with embeddings, RAG, and vector databases on AWS, applying MLflow, SageMaker Pipelines, and Weights & Biases to monitor,