AI Engineer - Hybrid

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.

• 19 leave credits for vacation and emergencies.

• 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: 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 AI Engineer is a hands-on technical role responsible for supporting the development, deployment, and maintenance of AI and machine learning models and pipelines that deliver tangible value to business operations. Working within the Data Analytics & AI team, this individual will contribute to AI/ML workstreams from experimentation through to production, helping ensure solutions are robust, scalable, and aligned with enterprise standards.

The ideal candidate is a developing engineer with a strong interest in applied AI, machine learning, and Generative AI. They are comfortable writing Python, working with data, learning modern AI/ML tooling, and collaborating with more experienced engineers to turn prototypes into reliable business solutions.

Job Responsibilities:

AI/ML Model Development & Deployment

• Contribute to the design, development, and deployment of 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, tested Python code, following the team's engineering standards and best practices, with support and review from senior team members where appropriate.

• Use Databricks, MLflow, and Azure AI services to support the development and operationalisation of scalable ML solutions.

MLOps & Pipeline Engineering

• Support the build and maintenance of ML pipelines covering data preparation, feature engineering, model training, evaluation, and deployment, using established CI/CD and Infrastructure as Code (IaC) practices.

• Assist with monitoring and maintenance of deployed models, including performance tracking, data quality checks, and investigation of issues.

• Contribute to improvements in the team's MLOps toolchain and processes, helping increase reliability, 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.

• Work with data engineers and the Data Platform Manager to understand data pipelines and feature requirements for AI/ML workloads.

Collaboration & Knowledge Sharing

• Work with business stakeholders, analysts, and the BI team to understand requirements and help translate them into clear AI/ML problem statements.

• Contribute to internal knowledge sharing through documentation, code reviews, demos, and team learning sessions.

• 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, software engineering with AI exposure, or applied AI required

• Good proficiency in Python and familiarity with core ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, Hugging Face) required

• Hands-on experience with Databricks, notebooks, and MLflow preferred; willingness to develop deeper platform expertise required

• Exposure to building Generative AI solutions (e.g., LLMs, RAG, prompt engineering) preferred

• Familiarity with Azure cloud services (e.g., Azure OpenAI, Azure ML, Azure Data Factory) preferred

• Understanding of MLOps principles, including model versioning, experiment tracking, CI/CD concepts, and production monitoring preferred

• Good 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 on technical challenges both independently and with guidance from more experienced engineers required

• Good written and verbal communication skills, with the ability to present technical findings to both technical and non-technical audiences required

• Eagerness to learn, develop technical depth, and grow into greater ownership of AI/ML solutions over time required