Senior AI Engineer - Hybrid

enablesGROUP; Pasig; Hybrid; PHP 1,000,000 - 1,800,000; Full time

Benefits offered by this job

Health and life insurance

20 leave credits

Career advancement

Laptop provided

Office in Pasig

Loyalty profit-sharing potential

In-house learning & development

Job summary

enablesGROUP is seeking a Senior AI Engineer to build, deploy, and maintain AI/ML models and pipelines. You will own AI/ML workstreams from development to production, ensuring robust, scalable solutions that meet enterprise standards.

The role emphasizes applied AI, prototyping Generative AI use cases, and strong software discipline in a collaborative environment. Hybrid schedule includes onsite Pasig office and remote options.

Qualifications

• 2+ years' experience in data science, machine learning engineering, or applied AI.
• Strong proficiency in Python and core ML libraries (scikit-learn, PyTorch, TensorFlow, Hugging Face).
• Hands‑on experience with Databricks (MLflow, notebooks, Unity Catalog).
• Practical experience building and deploying Generative AI solutions (LLMs, RAG, prompt engineering).
• Solid understanding of MLOps, including CI/CD for ML, model versioning, experiment tracking, and production monitoring.
• Strong software engineering fundamentals: Git, testing, code reviews.
• Familiarity with SQL and data modelling concepts (medallion architecture, star schema).
• Experience with cloud data platforms and regulated industries (life sciences/healthcare/pharma).
• Strong analytical and problem‑solving skills; good written and verbal communication.

Responsibilities

• Design, develop, and deploy ML and AI models across various use cases including NLP and Generative AI.
• Write clean, production‑quality Python code following engineering standards.
• Leverage Databricks (MLflow, NOTEBOOKS, Unity Catalog) and Azure AI services for scalable ML solutions.
• Build and maintain robust ML pipelines with CI/CD and IaC practices.
• Implement monitoring and alerting for deployed models including drift detection and retraining triggers.
• Contribute to MLOps toolchain and processes for efficiency and reproducibility.
• Conduct EDA and rapid prototyping to evaluate new AI/ML use cases.
• Design experiments to evaluate model performance and document findings.
• Collaborate with data engineers and BI to meet AI/ML workload requirements.
• Partner with stakeholders to translate requirements into ML problem statements.
• Share knowledge through documentation, code reviews, talks, and training.

Skills

Python

ML engineering

Generative AI

MLOps

Data analysis

Communication

Team collaboration

Problem solving

Tools

Databricks

MLflow

Unity Catalog

Azure AI

Git

CI/CD

Feature Store

Job description

Job Title: Senior AI Engineer

Work Schedule: Monday to Friday, 4:00 PM to 1:00 AM PH Time (Hybrid | 3x Onsite, 2x WFH)

Job Summary

The Senior AI Engineer is a hands‑on technical role responsible for building, deploying, and maintaining AI and machine learning models and pipelines that deliver tangible value to business operations. Working within the Data Analytics & AI team, this individual will take ownership of AI/ML workstreams from development through to production, ensuring solutions are robust, scalable, and aligned with enterprise standards.

The ideal candidate is a skilled engineer with a passion for applied AI and someone who is equally comfortable prototyping a new Generative AI use case as they are optimising a production ML pipeline. They bring strong software engineering discipline to data science, and thrive in collaborative, cross‑functional environments.

Benefits:

• Comprehensive health and life insurance starting Day 1, covering 2 eligible dependents.
• 20 leave credits for vacation, emergencies, sick days, and even your birthday.
• Endless opportunities for career advancement with annual performance reviews and salary increases.
• Company‑provided laptop to set you up for success.
• Convenient office location in Pasig, at the heart of Manila, accessible to all.
• Loyalty rewards: Employees celebrating 5 years could receive a profit‑sharing scheme.
• In‑house learning & development programs with access to the latest in AI and technology.

Job Responsibilities:

• Design, develop, and deploy machine learning and AI models across a range of use cases, including predictive analytics, NLP, classification, and Generative AI (e.g., LLM‑powered agents, RAG pipelines).
• Write clean, well‑tested, production‑quality Python code, adhering to the team's engineering standards and best practices.
• Leverage Databricks (including MLflow, Feature Store, and Model Serving) and Azure AI services to build and operationalise scalable ML solutions.
• Build and maintain robust ML pipelines covering data preparation, feature engineering, model training, evaluation, and deployment using CI/CD and Infrastructure as Code (IaC) practices.
• Implement monitoring and alerting for deployed models, including drift detection, performance tracking, and automated retraining triggers.
• Contribute to the continuous improvement of the team's MLOps toolchain and processes, ensuring efficiency and reproducibility.
• Conduct exploratory data analysis and rapid prototyping to assess the feasibility and potential impact of new AI/ML use cases.
• Design and run experiments to evaluate model performance, applying rigorous statistical methods and clear documentation of findings.
• Collaborate with data engineers and the Data Platform Manager to ensure data pipelines and feature sets meet the requirements of AI/ML workloads.
• Partner with business stakeholders, analysts, and the BI team to understand requirements and translate them into well‑defined AI/ML problem statements.
• Contribute to internal knowledge sharing through documentation, code reviews, tech talks, and training sessions to raise AI literacy across the organisation.
• Support the Lead AI Engineer in evaluating new tools, frameworks, and approaches, providing hands‑on technical input and proof‑of‑concept development.

Qualifications:

• 2+ years' experience in data science, machine learning engineering, or applied AI required.
• Strong proficiency in Python and core ML libraries (e.g., scikit‑learn, PyTorch, TensorFlow, Hugging Face) required.
• Hands‑on experience with Databricks (including MLflow, notebooks, and Unity Catalog) required.
• Practical experience building and deploying Generative AI solutions (e.g., LLMs, RAG, prompt engineering) strongly preferred.
• Solid understanding of MLOps principles, including CI/CD for ML, model versioning, experiment tracking, and production monitoring required.
• Strong software engineering fundamentals, including version control (Git), testing, and code review practices required.
• Familiarity with SQL and data modelling concepts (e.g., medallion architecture, star schema) preferred.
• Experience working with structured and unstructured data in a cloud data platform environment preferred.
• Experience within regulated industries (life sciences, healthcare, or pharma) preferred.
• Strong analytical and problem‑solving skills with the ability to work autonomously on complex technical challenges required.
• Good written and verbal communication skills, with the ability to present technical findings to both technical and non‑technical audiences required.