AI Engineer (Gen AI)

ESOL IT SERVICES INC.; Philippines; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

ESOL IT SERVICES INC. is seeking an AI/ML Engineer to design, build, and deploy advanced AI solutions that integrate with enterprise data platforms. You will work on predictive analytics, anomaly detection, and automation while developing Generative AI capabilities using LLMs and RAG architectures.

You will collaborate with data engineers and stakeholders to embed AI into data pipelines and dashboards, ensuring production readiness, governance, and interpretability across business units.

Qualifications

• Experience designing and deploying AI/ML solutions in enterprise environments.
• Proficiency with Python and SQL for data processing and model integration.
• Familiarity with MLOps, model monitoring, and CI/CD pipelines.

Responsibilities

• Design, build, and deploy AI/ML solutions that integrate with enterprise data products.
• Operationalize ML models for predictive analytics, anomaly detection, and automation.
• Develop Generative AI solutions using LLMs and RAG architectures.
• Collaborate with data engineers to embed AI in data pipelines with Fabric and Databricks.
• Ensure production readiness with monitoring, logging, and performance optimization.

Skills

AI/ML model development

Python/SQL

MLOps

Communication

Tools

Azure ML

Databricks

MLflow

Azure DevOps

Git

Job description

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 such as Microsoft Fabric and 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, including fairness, transparency, and 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, including Azure Machine Learning, Cognitive Services, and Azure OpenAI integration.

• Microsoft Fabric AI capabilities, including Copilot, AutoML, and intelligent insights.

• Databricks, including MLflow, Model Registry, and Delta Lake.

• Understanding of Lakehouse architecture and AI integration patterns.

Data Engineering & Integration

• Strong Python and/or SQL skills 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.

• 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 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.