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AI/ML Engineer
Career Insights for Machine Learning Engineer
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Based on Arizona data
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$138,790 / year median in Arizona
Job Description
Job Title:
: AI/ML Engineer
Location:
Phoenix, AZ (Day 1 onsite - Hybrid 3 days a week in office)
Duration:
Long Term Contract Responsibilities
Design, build, and enhance AI/ML and generative AI solutions using Python, large language models (LLMs), retrieval-augmented generation (RAG), prompt engineering, and agentic AI frameworks.
Partner with Security, Risk, Engineering, Governance, and Data teams to deliver secure, reliable, scalable, and responsible AI solutions.
Develop and support data-processing, model-deployment, and monitoring workflows.
Drive continuous improvements in code quality, system performance, scalability, maintainability, and operational reliability.
Minimum Qualifications
6+ years of hands-on professional experience in Python development.
Experience with one or more machine-learning frameworks, such as PyTorch, TensorFlow, or scikit-learn.
Practical knowledge of LLMs, natural language processing (NLP), embeddings, RAG, prompt engineering, and AI application development.
Experience with agentic AI frameworks or orchestration tools, such as LangChain, AutoGen, CrewAI, or comparable technologies.
Hands-on experience with SQL and data manipulation.
Working knowledge of REST APIs, Git-based development workflows, and core cloud concepts across AWS, Google Cloud, or Microsoft Azure.
Familiarity with containerization technologies, such as Docker, and CI/CD practices.
Exposure to MLOps, workflow orchestration, and data-processing technologies, such as MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or Apache NiFi.