Senior ML Engineer — Production ML & MLOps Lead

Remotedxb; Dubai; On-site; AED 360,000 - 660,000; Full time

Job summary

Janea Systems is seeking an experienced ML engineer to take ML and LLM proofs-of-concept into production with enterprise-grade reliability and cost characteristics. The role emphasizes building robust data pipelines, ML platform automation, deployment, and monitoring in a cloud environment.

You will design and harden training pipelines, implement CI/CD for ML systems, and work on retrieval-augmented generation (RAG) projects. Strong English communication and contractor setup are required.

Qualifications

• 6+ years of software engineering experience.
• 3+ years of experience shipping ML systems into production.
• Strong Python skills at production code quality.
• Deep expertise in at least one area: LLM/agent systems, ML platform/MLOps, or ML data/pipeline engineering.
• Proficiency with PyTorch or TensorFlow.
• Experience with Containers and Kubernetes in AWS, Azure, or GCP.
• Experience with workflow orchestration (Airflow, Kubeflow, Argo, Dagster, or Prefect).
• Ability to apply testing, benchmarking, and monitoring to ML systems.
• Client-facing English communication skills.
• Ability to work as an independent contractor with your own legal entity.

Responsibilities

• Take ML or LLM proof-of-concepts to production with enterprise-grade reliability and cost characteristics.
• Design and harden data and training pipelines for enterprise ML systems.
• Perform ML platform and MLOps work including deployment, versioning, monitoring, and CI/CD.
• Build LLM and RAG systems focusing on retrieval quality, evaluation, and cost control.
• Conduct technical audits and advisory work on existing ML stacks.

Skills

Software engineering

Production ML

Python proficiency

Tools

PyTorch

TensorFlow

Containers

Kubernetes

Airflow

Kubeflow

Dagster

Job description

Janea Systems is seeking an experienced ML engineer to take ML and LLM proofs-of-concept into production with enterprise-grade reliability and cost characteristics. The role emphasizes building robust data pipelines, ML platform automation, deployment, and monitoring in a cloud environment.

You will design and harden training pipelines, implement CI/CD for ML systems, and work on retrieval-augmented generation (RAG) projects. Strong English communication and contractor setup are required.