Hybrid AI Engineer: GenAI, MLOps & Pipelines

Tasq Work; Makati; Hybrid; PHP 900,000 - 1,500,000; Full time

Benefits offered by this job

Hybrid work

Night shift

Job summary

Tasq Work is seeking an AI Engineer to design, build, and operationalize AI-enabled data products and intelligent solutions that improve enterprise decision-making and automation.

The role blends data engineering, machine learning, and analytics to deploy production-ready AI solutions, govern models, and collaborate with product owners, data engineers, and architects in a hybrid setting at Eton, Centris.

Qualifications

• Experience designing and deploying AI/ML solutions in enterprise data environments.
• Ability to integrate AI models into data pipelines and lakehouse architectures.
• Familiarity with responsible AI practices and governance.

Responsibilities

• Design, build, and deploy AI/ML solutions that integrate with enterprise data products and pipelines.
• Develop and operationalize ML models and AI services for predictive analytics and automation.
• Design and implement Generative AI solutions using LLMs and RAG architecture.
• Collaborate with data engineers to embed AI in data workflows and platforms (Fabric, Databricks).
• Translate business needs into reusable AI components and models.
• Ensure AI readiness across data products with standardized integration and inference patterns.
• Implement monitoring, logging, and performance tuning for production AI solutions.
• Document models, pipelines, and assumptions for auditability and reuse.
• Participate in Agile delivery and backlog refinement.

Skills

Python

SQL

ML/AI concepts

MLOps

Data integration

Stakeholder communication

Problem solving

Tools

Azure AI Services

Databricks

Microsoft Fabric

LLMs

RAG architecture

CI/CD

Job description

Tasq Work is seeking an AI Engineer to design, build, and operationalize AI-enabled data products and intelligent solutions that improve enterprise decision-making and automation.

The role blends data engineering, machine learning, and analytics to deploy production-ready AI solutions, govern models, and collaborate with product owners, data engineers, and architects in a hybrid setting at Eton, Centris.