ML Engineer: Build Scalable, Production‑Ready AI

Devitechs; Texas; On-site; USD 90,000 - 120,000; Full time

Job summary

A leading technology firm in Wisconsin seeks an experienced machine learning engineer to build large-scale machine learning solutions for various tasks including classification and regression. The role involves developing automated training pipelines, collaborating with Data Scientists, and deploying ML models efficiently. Candidates should have experience in using cloud resources for ML performance tuning and be skilled in data validation and monitoring solutions. Competitive compensation is offered in an innovative work environment.

Qualifications

• Experience with large-scale machine learning solutions.
• Proven track record in developing automated training pipelines.
• Strong understanding of ML model performance tuning.

Responsibilities

• Build and deploy machine learning solutions for various tasks.
• Collaborate with Data Scientists to ensure production quality.
• Implement monitoring tools for model health tracking.

Skills

Machine Learning

Data Analysis

Cloud Computing (GPU/TPU)

API Development

Version Control

Tools

MLflow

Weights & Biases

Job description

✔️ Build large-scale machine learning solutions for classification, regression, clustering, and recommendation tasks.

✔️ Develop automated training pipelines, feature stores, and data transformations.

✔️ Implement ML systems capable of handling high-volume real-time data.

✔️ Create reusable libraries and frameworks for ML model development.

✔️ Perform model training, validation, and performance tuning using cloud GPU/TPU resources.

✔️ Collaborate with Data Scientists to transform prototypes into production systems.

✔️ Deploy ML models through REST APIs, streaming services, or serverless functions.

✔️ Implement monitoring tools to track model health, latency, and production failures.

✔️ Build robust data validation, testing, and quality control checks.

✔️ Maintain and optimize ML infrastructure for scalability and cost efficiency.

✔️ Use version control and experiment tracking tools like MLflow or Weights & Biases.

✔️ Incorporate responsible AI frameworks for fairness, transparency, and explainability.

✔️ Collaborate with business teams to integrate ML predictions into workflows.

✔️ Optimize models for edge devices, mobile platforms, or low-latency environments.

✔️ Troubleshoot production ML issues and perform root-cause analysis.

✔️ Benchmark model performance using industry-standard datasets and metrics.

✔️ Explore and implement deep learning, transformers, and generative AI solutions.

✔️ Document ML architectures, workflows, and project implementation details.

✔️ Participate in code reviews and mentoring of junior engineers.

✔️ Drive innovation by proposing new ML methodologies and tools.