Senior ML Engineer: Build Production‑Ready AI at Scale

SoftServe; Colombia; On-site; COP 90,000,000 - 150,000,000; Full time

Job summary

SoftServe is seeking an experienced ML Engineer to turn advanced ML ideas into reliable production solutions. You will own building and scaling end-to-end ML systems within the AI and Data Science Center of Excellence, collaborating with engineers, data scientists, and clients to deliver practical AI with measurable impact.

The role focuses on designing end-to-end pipelines, implementing LLMOps, agentic workflows, and robust CI/CD for ML in cloud environments, ensuring reliability and

Qualifications

• Advanced Python and ML programming skills are required.
• Hands-on experience with LLMOps, agentic systems, and experiment tracking tools.
• Strong CI/CD practices for ML systems and cloud experience.

Responsibilities

• Design end-to-end ML pipelines from data ingestion to production deployment.
• Build LLMOps pipelines and improve model reproducibility and monitoring.
• Collaborate with data scientists, engineers, and clients to deliver NLP and multimodal AI solutions.
• Develop agentic systems and multi-agent workflows for production scale.
• Enhance ML infra with CI/CD/CT, cloud, data stores, and security practices.
• Package ML services into production applications and ensure reliability.
• Automate and monitor pipelines with Databricks, Kubeflow, or Airflow.

Skills

Python

LLMOps

AgentOps

CI/CD for ML

Cloud platforms

LangGraph / CrewAI

English proficiency

Education

Master's degree in Computer Science

Tools

MLflow

Langfuse

LangSmith

Weights & Biases

Databricks Workflows

Kubeflow

Airflow

Job description

SoftServe is seeking an experienced ML Engineer to turn advanced ML ideas into reliable production solutions. You will own building and scaling end-to-end ML systems within the AI and Data Science Center of Excellence, collaborating with engineers, data scientists, and clients to deliver practical AI with measurable impact.

The role focuses on designing end-to-end pipelines, implementing LLMOps, agentic workflows, and robust CI/CD for ML in cloud environments, ensuring reliability and