Senior ML Engineer — Production AI & MLOps (Remote)

Smart Working Solutions; Pakistan; Remote; PKR 400,000 - 680,000; Full time

Job summary

Smart Working seeks a Senior ML Engineer to lead Applied AI initiatives, focusing on productionising ML models, NLP and generative AI systems, and building observable, scalable ML infrastructure.

You will develop production-ready components, pipelines and services using Python, FastAPI and modern tooling while ensuring model evaluation, safety and governance of AI outputs across real-time and batch workloads.

Qualifications

• 6+ years of professional AI/Machine Learning experience.
• 5+ years of professional MLOps experience.
• 2+ years of real Applied AI experience.
• Strong professional Python experience; Python is the core programming language for this role.
• Proven experience productionising and deploying AI/ML applications and models.
• Strong understanding of Applied AI/ML and MLOps, beyond pure research.

Responsibilities

• Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions.
• Build new ML components and re-engineer existing models into production-ready components.
• Develop production ML applications using Python and FastAPI.
• Build and maintain ML pipelines for integration, evaluation, deployment and operation.
• Design and automate model evaluation pipelines using golden datasets and quality thresholds.
• Implement guardrails to assess grounding and hallucination in generative AI outputs.

Skills

Python

ML Engineer

MLOps

NLP

Generative AI

RAG

Model evaluation

Observability

Backend engineering

Cloud platforms

Tools

Docker

Kubernetes

FastAPI

Argo Workflows

AWS

Azure

GCP

Job description

Smart Working seeks a Senior ML Engineer to lead Applied AI initiatives, focusing on productionising ML models, NLP and generative AI systems, and building observable, scalable ML infrastructure.

You will develop production-ready components, pipelines and services using Python, FastAPI and modern tooling while ensuring model evaluation, safety and governance of AI outputs across real-time and batch workloads.