AI/ML Engineer — Production Pipelines & MLOps

veritone; Irvine (CA); On-site; USD 175,000 - 200,000; Full time

Job summary

veritone is seeking an AI/ML Engineer to build, document, and refactor production‑grade AI/ML pipelines and model integration layers. You will work with Python, PyTorch/TensorFlow, and vector databases to optimize inference workflows and scaling.

The role requires hands‑on ML deployment experience, robust API design, and collaboration with cross‑functional teams to deliver intelligent services across applications. A strong emphasis on testable, maintainable code is expected.

Qualifications

• 3+ years of professional experience building and deploying software systems with AI/ML models in production.
• Proficiency with Python and ML libraries (PyTorch, NumPy, Pandas, Scikit-learn, Hugging Face).
• Hands-on experience with LLMs, RAG architectures, prompt engineering, or ML model pipelines and inference serving.
• Design and implementation of robust APIs and backend microservices in Python (Go/Node.js a plus).

Responsibilities

• Identify and optimize existing code, model inference workflows, and data pipelines for latency improvements.
• Collaborate with product, design, data, and infrastructure teams to integrate AI/ML into applications.
• Participate in on-call rotation for production ML services as needed.
• Share knowledge in team meetings and stay current with AI trends.
• Contribute to meeting commitments and be open to feedback.
• Write maintainable code with tests, benchmarks, and readable implementations.

Skills

Python

PyTorch

NumPy

Pandas

HuggingFace

LLMs

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

MLflow

Weights & Biases

Pinecone

Qdrant

pgvector

Elasticsearch

Postgres

Job description

veritone is seeking an AI/ML Engineer to build, document, and refactor production‑grade AI/ML pipelines and model integration layers. You will work with Python, PyTorch/TensorFlow, and vector databases to optimize inference workflows and scaling.

The role requires hands‑on ML deployment experience, robust API design, and collaboration with cross‑functional teams to deliver intelligent services across applications. A strong emphasis on testable, maintainable code is expected.