Senior MLOps Engineer: Build Production-Scale ML Pipelines

EPAM Systems; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

EPAM Systems in Singapore is seeking a Senior Software Engineer (MLOps) to bring machine learning into production at scale. You will build ML and LLM pipelines, standardize model lifecycle management and help teams ship reliable AI features using Python, container platforms and cloud services.

You will own end-to-end pipelines, manage model lifecycles, operationalize LLMs and refactor research prototypes into production-grade code with emphasis on testing, performance and observability.

Qualifications

• Strong Python background; write clean, testable code.
• Production ML systems experience incl. CI/CD and release workflows.
• Experience deploying LLMs, embeddings, and RAG.
• Docker and Kubernetes for packaging/running workloads.
• Familiar with experiment tracking and model registry tools (MLflow, W&B).
• Cloud compute and GPU fundamentals for training/inference optimization.

Responsibilities

• Build end-to-end ML and LLM pipelines from ingestion to training, serving and CI/CD.
• Own model lifecycle workflows including experimentation, registry, deployments, promotions and monitoring.
• Operationalize large language models, embeddings, RAG and agentic workflows.
• Refactor research prototypes into production-grade Python with testing and debugging.
• Containerize services using Docker and orchestrate workloads with Kubernetes.
• Define observability for AI systems including drift, quality metrics and reliability signals.
• Partner with data science and platform teams to integrate AI into shared services.
• Contribute to engineering standards for security, compliance and reproducibility.

Skills

Python

CI/CD

ML workflow

LLMs embeddings

Cross-team collaboration

Tools

Docker

Kubernetes

MLflow

Weights & Biases

GitLab CI

Job description

EPAM Systems in Singapore is seeking a Senior Software Engineer (MLOps) to bring machine learning into production at scale. You will build ML and LLM pipelines, standardize model lifecycle management and help teams ship reliable AI features using Python, container platforms and cloud services.

You will own end-to-end pipelines, manage model lifecycles, operationalize LLMs and refactor research prototypes into production-grade code with emphasis on testing, performance and observability.