Data Scientist- ML Engineering at WIZELINE

, , Colombia -

Full Time

Start Date

Immediate

Expiry Date

26 Mar, 26

Salary

0.0

Posted On

26 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML Engineering, MLOps, Large-Scale ML Systems, Spark, Azure Databricks, MLflow, Kubernetes, Docker, CI/CD Pipelines, Cloud Environments, Hybrid Architectures, Model Governance, Monitoring Systems, Model Drift, Containerization, Observability

Industry

IT Services and IT Consulting

Description

We are: Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact. With the right people and the right ideas, there’s no limit to what we can achieve Are you a fit? Sounds awesome, right? Now, let’s make sure you’re a good fit for the role: Key Responsibilities Architect end-to-end ML infrastructure, including pipelines, model serving, monitoring, and governance. Lead deployment of high-impact ML solutions such as forecasting engines, optimization models, and NLP use cases. Design and manage advanced CI/CD workflows using Azure Pipelines, MLflow, and Databricks. Implement model registry, versioning, lineage, and audit-compliant governance frameworks. Build and maintain monitoring systems to detect model drift and automate retraining cycles. Mentor MLOps engineers and collaborate with platform, data, and product teams to ensure seamless integration. Drive adoption of MLOps best practices across containerization, observability, testing, and scalable infrastructure. Must-have Skills 5–8+ years of experience in ML Engineering, MLOps, or building large-scale ML systems. Strong expertise with Spark, Azure Databricks, MLflow, Kubernetes, and Docker. Proven track record deploying ML solutions at enterprise scale with audit, governance, and monitoring layers. Experience designing ML infrastructure and CI/CD pipelines in cloud environments. Knowledge of hybrid or multi-cloud architectures. Bachelor’s degree required; Master’s preferred in Computer Science, Engineering, or related fields. Nice-to-have: AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows. What we offer: A High-Impact Environment Commitment to Professional Development Flexible and Collaborative Culture Global Opportunities Vibrant Community Total Rewards *Specific benefits are determined by the employment type and location. Find out more about our culture here.

Responsibilities

Architect end-to-end ML infrastructure and lead the deployment of high-impact ML solutions. Collaborate with teams to ensure seamless integration and drive adoption of MLOps best practices.