Machine Learning Engineer

Veriipro; Seattle (WA); On-site; USD 150,000 - 210,000; Full time

Job summary

Veriipro in Seattle seeks a senior machine learning engineer to design, develop, and maintain high-performance distributed systems for large-scale inference and data processing. You will build scalable ML pipelines for model training, deployment, monitoring, and lifecycle management, and design frameworks for multi-agent AI systems with robust state management.

Collaborating with cross-functional teams, you will optimize RAG pipelines, implement MLOps practices, and deploy AI-powered products in

Qualifications

• Bachelor's degree or equivalent practical experience in a relevant field.
• 5+ years in machine learning engineering or related roles.
• Experience building distributed systems and scalable backend architectures.
• Deep ML lifecycle knowledge: data ingestion, training, evaluation, deployment, monitoring.
• Hands-on with LLMs, RAG, and advanced prompt engineering.
• Production ML deployment and model maintenance experience.
• Strong Python programming.
• Experience with PyTorch and modern ML frameworks.
• Strong software engineering practices: testing, version control, and code quality.
• Excellent analytical, problem-solving, and communication skills.

Responsibilities

• Design, develop, and maintain high-performance distributed systems for ML inference and data processing.
• Build scalable ML pipelines for model training, deployment, monitoring, and lifecycle management.
• Design and implement frameworks for multi-agent AI systems with robust state management.
• Enhance RAG pipelines and context management to improve model accuracy and relevance.
• Develop platform tools for prompt engineering, evaluation, and experimentation.
• Deploy, monitor, and maintain ML models in production environments.
• Implement MLOps practices including versioning, observability, and automated pipelines.
• Collaborate with cross-functional teams to deliver AI-powered products and services.
• Optimize system performance, scalability, and reliability for high-volume workloads.
• Stay current with emerging ML frameworks and best practices.

Skills

Python

Distributed systems

Machine learning

LLMs

Prompt engineering

MLOps

Backend architectures

Software engineering

Testing & version control

Education

Bachelor's degree in CS or related field

Tools

PyTorch

Git

Docker

Kubernetes

Job description

Responsibilities

• Design, develop, and maintain high-performance distributed systems to support large-scale machine learning inference and data processing.


• Build and optimize scalable machine learning pipelines for model training, deployment, monitoring, and lifecycle management.


• Design and implement frameworks for multi-agent AI systems, emphasizing state management, reliability, and long-running autonomous workflows.


• Architect and enhance Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy, relevance, and response quality.


• Develop platform-level tools for prompt engineering, automated evaluation, prompt optimization, and experimentation.


• Deploy, monitor, and maintain machine learning and generative AI models in production environments.


• Implement robust MLOps practices, including model versioning, observability, monitoring, and automated deployment pipelines.


• Collaborate with cross-functional teams to design, develop, and deliver AI-powered products and services.


• Optimize system performance, scalability, and reliability for high-volume production workloads.


• Stay current with emerging technologies, frameworks, and best practices in machine learning and generative AI.

Required Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Software Engineering, or a related field (or equivalent practical experience).


• 5+ years of experience in machine learning engineering, software engineering, or related technical roles.


• Strong experience designing and developing distributed systems and scalable backend architectures.


• Deep understanding of the end-to-end machine learning lifecycle, including data ingestion, model training, evaluation, deployment, monitoring, and maintenance.


• Hands‑on experience building applications using Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG) architectures and advanced prompt engineering techniques.


• Experience deploying, scaling, and maintaining machine learning models in production environments.


• Strong programming skills in Python.


• Experience with modern machine learning frameworks such as PyTorch.


• Strong understanding of software engineering best practices, including testing, version control, and code quality.


• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with distributed task queues or workflow orchestration frameworks for managing complex, multi-stage AI processes.


• Experience with frameworks that support horizontal scaling of compute-intensive machine learning workloads.


• Knowledge of agentic AI architectures, including multi-agent systems, tool integration, self-correction, and iterative reasoning workflows.


• Familiarity with vector databases, embedding technologies, and high-throughput data processing pipelines.


• Experience implementing MLOps practices, CI/CD pipelines, and cloud-based machine learning infrastructure.


• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.


• Experience with containerization and orchestration technologies such as Docker and Kubernetes.