Why Join enablesGROUP?
Since 2016, enablesGROUP has been on a mission: to deliver high-quality operations and outsourcing services to every client, big or small.
Fast forward to 2026, we’ve grown our global footprint to serve 100+ clients and expanded into 4 key industries. At enablesGROUP, you’re not just joining a company – you’re joining a community that values growth, learning, and success. Check us out at www.enablesgroup.com.
At enablesGROUP, you’re not just joining a company – you’re joining a community that values growth, learning, and success.
We have market leading engagement scores and invest heavily in your Learning and Development, with a specific focus on enhancing your ability to leverage AI in your daily tasks.
Our Perks & Benefits include:
- Comprehensive health and life insurance starting Day 1, covering 2 eligible dependents.
- 20 leave credits for vacation and emergency.
- 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 Title: Senior AI Engineer
Location: Ortigas, Pasig, PH
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.
Job Responsibilities:
AI/ML Model Development & Deployment
- 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.
MLOps & Pipeline Engineering
- 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.
Data Exploration & Experimentation
- 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.
Collaboration & Knowledge Sharing
- 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
- Experience with Azure cloud services (e.g., Azure OpenAI, Azure ML, Azure Data Factory) required
- 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