Senior Applied AI/ML Engineer: Build & Deploy AI

Atlas Technologies, Inc.; Des Moines (IA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Competitive salary

Generous benefits package

Inclusive workplace

Job summary

Atlas Tech is seeking a Senior Applied AI/ML Engineer to design, build, and deploy advanced AI-enabled applications in production. You will lead end-to-end ML pipelines, integrate LLM tools, and partner with product and domain teams to deliver measurable value.

The ideal candidate combines research, prototyping, and applied engineering, with strong Python and ML library experience, and a track record of deploying models in production environments.

Qualifications

• Experience deploying AI/ML models to production.
• Strong Python programming and data analysis skills.
• Ability to translate business needs into technical requirements.

Responsibilities

• Design, build, and deploy scalable AI/ML tools for generative AI use cases.
• Develop end-to-end ML pipelines, including LLM tool integration, data ingestion, feature engineering, model training, evaluation, and monitoring
• Implement production-grade inference systems and integrate ML models into applications, APIs, and microservices
• Lead experimentation, define hypotheses, run evaluations, assess model performance, and identify improvement strategies
• Work closely with engineering teams to build robust, secure, and maintainable AI-enabled architectures
• Mentor junior engineers and contribute to internal standards, coding practices, and reusable components

Skills

Python

ML pipelines

LLMs

Model deployment

Experimentation

Communication with stakeholders

Cloud platforms

Data science basics

Education

Bachelor's or Master's in CS/ML/DS/Engineering

Tools

TensorFlow

PyTorch

MLflow

SageMaker

Vertex AI

LangChain

Docker

Kubernetes

Job description

Atlas Tech is seeking a Senior Applied AI/ML Engineer to design, build, and deploy advanced AI-enabled applications in production. You will lead end-to-end ML pipelines, integrate LLM tools, and partner with product and domain teams to deliver measurable value.

The ideal candidate combines research, prototyping, and applied engineering, with strong Python and ML library experience, and a track record of deploying models in production environments.