Senior AI/ML Engineer — Build Production-Ready AI Solutions

Atlas Technologies, Inc.; West Virginia; On-site; USD 140,000 - 190,000; Full time

Job summary

Atlas Technologies, Inc. seeks a Senior Applied AI-Machine Learning Engineer to design, build, and operationalize advanced AI/ML solutions, deploying them into production and collaborating with product, engineering, and domain teams to deliver value.

You will shape architectures, select models, and run experiments, translating complex concepts into usable capabilities. This role blends research with practical engineering across end‑to‑end pipelines.

Qualifications

• Bachelor’s or Master’s in CS/DS/Engineering required.
• 3+ years of applied AI/ML engineering with production deployments.
• Experience with LLMs, fine‑tuning, RAG, and prompt engineering.
• Proficiency in Python and ML libraries; API/ML workflow basics.

Responsibilities

• Design, build, and deploy scalable AI/ML tools for generative AI use cases.
• Develop end-to-end ML pipelines, including data ingestion, feature engineering, and model training.
• Implement production‑grade inference systems and integrate models into apps and microservices.
• Lead experiments, evaluate model performance, and drive improvements.
• Collaborate with engineering teams on secure, maintainable AI architectures.
• Mentor juniors and contribute to internal standards and reusable components.
• Ensure responsible AI practices, data governance, and bias evaluation.
• Prepare demos and proofs‑of‑concept integrations to illustrate value.

Skills

Python

Research & prototyping

Communication with execs

Education

Bachelor's or Master's in Computer Science, Data Science, Engineering

Tools

TensorFlow

PyTorch

scikit-learn

Hugging Face

LangChain

MLflow

SageMaker / Vertex AI / Databricks

Job description

Atlas Technologies, Inc. seeks a Senior Applied AI-Machine Learning Engineer to design, build, and operationalize advanced AI/ML solutions, deploying them into production and collaborating with product, engineering, and domain teams to deliver value.

You will shape architectures, select models, and run experiments, translating complex concepts into usable capabilities. This role blends research with practical engineering across end‑to‑end pipelines.