Senior ML Engineer — Remote Production AI & MLOps

Openkrill; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Smart Working is seeking a Senior ML Engineer with strong experience in Applied AI, Machine Learning and MLOps to build and modernise an AI platform. The role combines Applied AI, MLOps and backend engineering with a focus on productionising, deploying, evaluating and operating ML/AI systems.

You will blend NLP and generative AI capabilities with reliable, observable infrastructure. You will design, implement and operate ML pipelines, evaluate models, and ensure safe deployment with robust

Qualifications

• 6+ years of professional AI/ML experience with production use-cases.
• 5+ years of professional MLOps experience.
• 2+ years of Applied AI beyond experiments or personal projects.
• Strong Python experience for production ML apps.

Responsibilities

• Refactor, modernise and productionise ML models and Applied AI capabilities including NLP and generative AI.
• Build new ML components and modular production-ready models.
• Develop production ML applications and supporting services using Python.
• Build and maintain reliable ML pipelines for model integration, evaluation, deployment and operation.
• Design and automate model evaluation pipelines with golden datasets and performance thresholds.
• Implement guardrails to assess grounding and hallucinations in generative AI outputs.
• Design mechanisms for model and data provenance to support auditability.

Skills

Python

AI/ML

MLOps

Applied AI

Production ML

Tools

FastAPI

Docker

Kubernetes

AWS

Azure

GCP

Job description

Smart Working is seeking a Senior ML Engineer with strong experience in Applied AI, Machine Learning and MLOps to build and modernise an AI platform. The role combines Applied AI, MLOps and backend engineering with a focus on productionising, deploying, evaluating and operating ML/AI systems.

You will blend NLP and generative AI capabilities with reliable, observable infrastructure. You will design, implement and operate ML pipelines, evaluate models, and ensure safe deployment with robust