Senior Machine Learning Engineer

Jobtailor; North Carolina; On-site; USD 150,000 - 190,000; Full time

Job summary

Jobtailor in the United States is seeking an experienced software/ML/AI engineer to design, build, and deliver production-ready AI capabilities, services, and applications. You will develop scalable APIs, microservices, and backend systems for AI-powered products and workflows, collaborating with product, engineering, data, and platform teams.

The role involves building AI infrastructure, deployment pipelines, monitoring, observability, and automated testing; prototyping emerging AI technologies

Qualifications

• Bachelor’s degree in a relevant field; or advanced degree with relevant experience.
• 5+ years of professional software/ML/AI engineering experience, with meaningful production ownership.
• Strong software engineering fundamentals and a track record of designing, building, deploying and maintaining production systems.
• Strong Python proficiency.
• Experience with backend/application languages such as C#, Java, or TypeScript is valuable.
• Experience building APIs, backend services, microservices, data integrations, or distributed systems.
• Hands-on experience with cloud infrastructure, containers, and production deployment.
• Kubernetes/Docker/MLOps experience is particularly valuable.
• Experience building or operating ML/AI systems in production, including evaluation, monitoring, observability, and deployment pipelines.
• Exposure to LLM applications, RAG, agentic workflows, model/tool integration, or similar modern AI patterns.
• Ability and willingness to work across AI, backend, infrastructure, data, and occasionally UI/application code.
• Strong ownership, curiosity, and comfort operating in an environment where patterns and solutions are still being established.
• Fluent written and oral communication in English.
• Authorized to work for any employer in the U.S.
• Employment visa sponsorship is unavailable.

Responsibilities

• Design, build, and deliver production-ready AI capabilities, services, and applications
• Develop scalable APIs, microservices, data integrations, and backend systems for AI-powered products and workflows
• Build and improve AI infrastructure, including evaluation frameworks, deployment pipelines, monitoring, observability, and automated testing
• Prototype and evaluate emerging AI technologies, including LLMs and agentic workflows
• Turn successful AI prototypes into reliable, enterprise-grade solutions
• Work across cloud infrastructure, data platforms, and application layers
• Integrate AI capabilities into the broader Q2 technology ecosystem
• Collaborate with product, engineering, data, and platform teams
• Define solutions, make technical tradeoffs, and take ideas from exploration through production

Skills

Python Proficiency

AI Systems Development

Cloud Infrastructure Experience

Microservices Architecture

Kubernetes/Docker/MLOps

Education

Bachelor’s degree in a relevant field

Advanced degree in a related field

Tools

Kubernetes

Docker

Cloud Platforms

MLOps

Data Platforms

Job description

• Design, build, and deliver production-ready AI capabilities, services, and applications
• Develop scalable APIs, microservices, data integrations, and backend systems for AI-powered products and workflows
• Build and improve AI infrastructure, including evaluation frameworks, deployment pipelines, monitoring, observability, and automated testing
• Prototype and evaluate emerging AI technologies, including LLMs and agentic workflows
• Turn successful AI prototypes into reliable, enterprise-grade solutions
• Work across cloud infrastructure, data platforms, and application layers
• Integrate AI capabilities into the broader Q2 technology ecosystem
• Collaborate with product, engineering, data, and platform teams
• Define solutions, make technical tradeoffs, and take ideas from exploration through production

Requirements

• Bachelor’s degree in a relevant degree and a minimum of 5 years of related experience; or an advanced degree with 3+ years of experience; or equivalent related work experience
• 5+ years of professional software/ML/AI engineering experience, with meaningful production ownership
• Strong software engineering fundamentals and a track record of designing, building, deploying and maintaining production systems
• Strong Python proficiency
• Experience with backend/application languages such as C#, Java, or TypeScript is valuable
• Experience building APIs, backend services, microservices, data integrations, or distributed systems
• Hands‑on experience with cloud infrastructure, containers, and production deployment
• Kubernetes/Docker/MLOPs experience is particularly valuable
• Experience building or operating ML/AI systems in production, including evaluation, monitoring, observability, and deployment pipelines
• Exposure to LLM applications, RAG, agentic workflows, model/tool integration, or similar modern AI patterns
• Ability and willingness to work across AI, backend, infrastructure, data, and occasionally UI/application code
• Strong ownership, curiosity, and comfort operating in an environment where patterns and solutions are still being established
• Fluent written and oral communication in English
• Authorized to work for any employer in the U.S.
• Employment visa sponsorship is unavailable

Core Competencies

Demonstrates expertise in designing and deploying AI capabilities and production systems, with strong proficiency in Python and experience in cloud infrastructure, microservices, and ML/AI systems. Capable of collaborating across teams to integrate AI solutions into existing technology ecosystems.

Highest-signal resume keywords

• Python Proficiency
• AI Systems Development
• Cloud Infrastructure Experience
• Microservices Architecture
• Kubernetes/Docker/MLOps

Hard Skills

• Software Engineering Fundamentals
• API Development
• Backend Development
• Data Integration
• Production Deployment
• Microservices
• Distributed Systems
• ML/AI Systems
• Evaluation Frameworks
• Automated Testing

Soft Skills

• Strong Ownership
• Curiosity
• Effective Communication

Industry Keywords

• AI Capabilities
• LLM Applications
• Agentic Workflows
• Production Systems
• Technical Tradeoffs

Tools & Technologies

• Kubernetes
• Docker
• Cloud Infrastructure
• MLOps
• Data Platforms