AI Engineer

Lancesoft APAC; Manila; On-site; PHP 1,200,000 - 2,400,000; Full time

Job summary

Lancesoft APAC is seeking an AI Engineer to design and operationalize AI-enabled data products within enterprise data platforms. You will bridge data engineering, ML, and analytics to deliver scalable AI solutions aligned with governance and Responsible AI practices.

You will collaborate with product owners, data engineers, and architects to embed AI capabilities into pipelines, ensure production readiness, explainability, and business-facing insights across dashboards and operating contexts.

Qualifications

• Strong problem-solving and analytical thinking required.
• Experience with AI/ML lifecycle and deployment recommended.
• Ability to communicate complex concepts to non-technical stakeholders.

Responsibilities

• Design, build, and deploy AI/ML solutions within enterprise data products and pipelines.
• Operationalize AI services for predictive analytics, anomaly detection, and automation.
• Design Generative AI solutions using LLMs, RAG, and prompt engineering.
• Collaborate with data engineers to embed AI in data platforms (Fabric, Databricks).
• Translate business needs into reusable AI components and governance practices.
• Ensure AI readiness and production-readiness with monitoring and explainability.

Skills

Strong problem-solving

Analytical thinking

Effective communication

Collaborative

Agile mindset

Documentation discipline

Continuous learning

Tools

Python

SQL

Azure DevOps

Git

Databricks

MLflow

Delta Lake

ServiceNow

Job description

Position Overview:

The AI Engineer role is responsible for designing, building, and operationalizing AI-enabled data products and intelligent solutions that enhance enterprise decision-making, automation, and analytics capabilities.

This role bridges data engineering, machine learning, and analytics by integrating AI models into enterprise data platforms and workflows. The AI Engineer collaborates with product owners, data engineers, and architects to develop scalable, governed, and production-ready AI solutions aligned with enterprise standards and Responsible AI practices.

The position focuses on enabling AI readiness across data products, embedding intelligence into pipelines, and ensuring that AI-driven insights are reliable, explainable, and actionable within business and operational contexts.

Overview of Work:

• 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

Cloud AI & Data Platforms

• Azure AI services (Azure Machine Learning, Cognitive Services, OpenAI integration)
• Microsoft Fabric AI capabilities (Copilot, AutoML, intelligent insights)
• Databricks (MLflow, Model Registry, Delta Lake)
• Understanding of Lakehouse architecture and AI integration patterns

Data Engineering & Integration

• 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

  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
  • Strong documentation and knowledge-sharing discipline
  • 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