Al/Ml Engineer

Provision People; Makati; On-site; PHP 2,790,000 - 4,241,000; Full time

Job summary

Provision People is seeking an AI/ML Engineer to design, build, and deploy AI/ML solutions that integrate with enterprise data products and lakehouse architectures, and to operationalize models for predictive analytics, anomaly detection, and automation.

You will work with data engineers, product owners, and stakeholders to translate business needs into reusable AI components, ensure production readiness, governance, and responsible AI practices across projects.

Qualifications

• Experience designing and deploying enterprise AI/ML solutions.
• Strong background in ML lifecycle, feature engineering, and evaluation.
• Proficiency in Python and SQL for data processing and integration.
• Hands-on with model serving, monitoring, and retraining strategies.
• Familiarity with Azure DevOps and Git workflows.

Responsibilities

• Design, build, and deploy AI/ML solutions that integrate with enterprise data products and lakehouse architectures.
• Operationalize models for predictive analytics, anomaly detection, and automation.
• Design and implement Generative AI solutions using LLMs, including RAG architectures.
• Collaborate with data engineers to embed AI capabilities into data pipelines and data platforms.
• Partner with product owners and stakeholders to translate business needs into AI components.
• Ensure AI readiness by standardizing model integration and inference patterns.
• Implement monitoring, logging, and performance optimization for production AI solutions.
• Translate model outputs into business-facing insights with analytics teams.
• Contribute to enterprise AI governance with responsible AI principles.
• Document AI models, features, pipelines, and assumptions for reuse and auditability.
• Participate in Agile delivery: backlog refinement, sprint planning, continuous improvement.

Skills

Machine learning model development

Feature engineering

Model training and deployment

Python/SQL

MLOps & deployment

Data pipelines integration

LLM & Generative AI

Lakehouse architectures

AI governance & Responsible AI

Tools

Azure DevOps (ADO)

Git-based version control

Job description

• Design, build, and deploy AI/ML solutions that integrate with enterprise data products, pipelines, and lakehouse architectures.
• Develop and operationalize machine learning models and AI services for use cases such as predictive analytics, anomaly detection, and automation.
• Design and implement Generative AI solutions using LLMs, including RAG architecture and prompt engineering.
• Collaborate with data engineers to embed AI capabilities into data pipelines and ensure seamless integration with data platforms (e.g., Fabric, Databricks).
• Partner with product owners, architects, and stakeholders to translate business needs into AI-driven solutions and reusable components.
• Enable AI readiness across DL&I data products by standardizing model integration, feature engineering, and inference patterns.
• Ensure AI solutions are production-ready by implementing monitoring, logging, and performance optimization practices.
• Support integration of AI outputs into data products, dashboards, and business processes, ensuring interpretability and usability.
• Work with analytics and reporting teams to translate model outputs into business-facing insights and metrics.
• Contribute to enterprise AI governance by ensuring compliance with Responsible AI principles (fairness, transparency, accountability).
• Document AI models, features, pipelines, and assumptions to support reuse, auditability, and knowledge sharing.
• Participate in Agile delivery practices including backlog refinement, sprint planning, and continuous improvement.

TECHNICAL SKILLS

AI & Machine Learning Engineering

• Machine learning model development and lifecycle management
• Feature engineering, model training, evaluation, and deployment
• Familiarity with supervised and unsupervised learning techniques
• Experience with model serving and inference pipelines
• Microsoft Fabric AI capabilities (Copilot, AutoML, intelligent insights)
• Understanding of Lakehouse architecture and AI integration patterns
• Strong Python and/or SQL for data processing and model integration
• Experience with data pipelines and orchestration tools
• Knowledge of data transformation and feature pipelines
• Integration of AI outputs into downstream analytics systems

MLOps & Deployment

• CI/CD pipelines for machine learning models
• Model versioning, monitoring, and retraining strategies
• Logging, observability, and performance tuning of AI solutions

Delivery & Tooling

• Azure DevOps (ADO) for backlog and work tracking
• Git-based source control for code and model artifacts
• Experience with collaborative development workflows

SOFT SKILLS:

• Strong problem-solving and analytical thinking, with a structured and detail-oriented approach
• Ability to translate complex technical concepts into business-relevant insights
• Effective communication across technical and non-technical stakeholders
• Strong collaboration skills across product, engineering, and architecture teams
• Influencing skills to promote AI adoption and data-driven practices
• Continuous learning mindset, especially in rapidly evolving AI technologies
• Comfortable working in Agile, fast-paced delivery environments

DOMAIN KNOWLEDGE:

• Understanding of enterprise data platforms and lakehouse architectures
• Familiarity with IT operational data and enterprise analytics use cases
• Experience with ServiceNow, its architecture, and data
• Awareness of data governance, data quality, and compliance considerations
• Experience with integrating AI solutions into enterprise workflows and systems
• Understanding of Responsible AI principles including fairness, transparency, bias mitigation, and auditability
• Exposure to enterprise-scale data environments and performance considerations