AI / ML Engineer

TechRuiter; United States; On-site; USD 110,000 - 140,000; Full time

Job summary

A cutting-edge technology company in the United States is seeking a hands-on AI/ML Engineer. The ideal candidate will build and deploy production-grade ML systems from the ground up, creating scalable ML pipelines and MLOps infrastructure. You'll need proven experience with Python, cloud platforms, and deploying ML models. Comfort with ambiguous requirements in a fast-paced environment is essential for success in this role, as well as expertise in relevant ML domains.

Qualifications

• 5+ years of experience with Python in production.
• Proven experience with multiple ML models deployed at scale.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.

Responsibilities

• Build and deploy production-grade ML systems from scratch.
• Develop scalable ML pipelines and MLOps infrastructure.
• Monitor models serving real users at scale.

Skills

Python

ML model deployment

Cloud platforms

SQL

NoSQL/vector databases

Bash and PowerShell scripting

NLP/LLMs experience

Time series analysis

Recommender systems

Job description

We’re seeking a hands‑on AI/ML Engineer who thrives in ambiguity and enjoys building production ML systems from scratch. You’ll own greenfield projects end‑to‑end, delivering scalable models and pipelines across domains such as NLP/LLMs, time series, and recommendation systems.

What You’ll Do

• Build and deploy production‑grade ML systems (0→1)
• Develop scalable ML pipelines, MLOps infrastructure, and internal tooling
• Deploy and monitor models serving real users at scale
• Work CLI‑first in a fast‑moving, low‑process environment

Required Skills

• Python (3–5+ years) in production
• Proven experience deploying multiple ML models to production
• Cloud platforms (AWS, Azure, GCP, or OCI)
• SQL and NoSQL/vector databases
• Bash and PowerShell scripting
• Strong experience in 2–3 ML domains (NLP/LLMs, time series, recommender systems, MLOps, or distributed training)

Ideal Profile

• Comfortable with vague requirements and rapid iteration
• Greenfield‑focused, production‑oriented, CLI‑first

Nice to Have

• CI/CD pipelines, computer vision, Go/Rust, model optimization, streaming data, or open‑source contributions