AI Engineer

D2B; Philippines; On-site; PHP 1,219,000 - 1,460,000; Full time

Job summary

D2B is seeking an AI Engineer in the Philippines to design, develop, and deploy production-grade AI/ML solutions for Australian enterprise clients. You will collaborate with the founder and client teams from scoping to go-live and handover, turning business problems into scalable technical systems.

You will leverage LLMs, traditional ML, data pipelines, and cloud deployment to deliver robust solutions, with responsibilities spanning architecture, integration, testing, and training for sustained

Qualifications

• Bachelor's or Master's in computer science, engineering, mathematics or physics, or equivalent practical experience.
• Advanced Python, including testing, packaging and maintainable code.
• Experience building AI applications that run in production.

Responsibilities

• Design solution architecture selecting models, RAG, agents or custom solutions based on client problem and budget.
• Integrate LLMs into client systems including prompt design and tool use.
• Train, validate and deploy ML models for problems like demand forecasting and anomaly detection.
• Analyse client data to test feasibility and build data pipelines feeding models.
• Evaluate production solutions for accuracy, drift, latency and cost; optimize as needed.
• Write secure, tested code with guardrails against prompt injection and data leakage.
• Translate business problems into technical requirements and advise client proposals.
• Hand over solutions with documentation and training for client teams.

Skills

Advanced Python

SQL

Data pipelines

LLM integration

Cloud deployment

Education

Bachelor's or Master's in CS/Engineering/Math/Physics

Tools

Azure

AWS

Docker

CI/CD

Claude/Codex

Job description

Position : AI Engineer

Salary : Up to PHP 120,000/month

Working Hours & Conditions: Observing AU working hours

Holidays : Observing AU holidays

A boutique consulting firm is looking for an AI engineer to build and deploy production-grade AI and machine learning solutions for enterprise clients. You will turn a client's business problem into a system that fits their needs and that their team can run and maintain after the engagement ends. Clients are listed and privately owned Australian businesses in retail, consumer goods, energy and mining. The work covers LLM applications and classical machine learning, and it includes the data pipelines underneath both. You will work directly with the founder and with client teams, across scoping through to

go-live and handover.

RESPONSIBILITIES:

• Design the solution architecture, choosing between off-the-shelf models, RAG, agents and custom models based on the client's problem and budget.
• Integrate LLMs into client systems, including prompt design, tool use and agentic workflows.
• Train, validate and deploy machine learning models for problems such as demand forecasting and anomaly detection.
• Analyse client data to test whether a use case is feasible, and build the data pipelines that feed models and applications.
• Evaluate and monitor solutions in production for accuracy, drift, latency and cost, and optimise them as issues appear.
• Write tested, secure code, with guardrails against prompt injection and data leakage.
• Work with client executives, business users and IT teams to turn business problems into technical requirements and give input into the firm's proposals.
• Hand over solutions with documentation and training so that client teams can run them

• 5+ years of professional software engineering, including at least two years building AI applications that run in production.
• Consulting or professional services experience, delivering to external clients against a fixed scope.
• A bachelor's or master's degree in computer science, engineering, mathematics or physics, or equivalent practical experience.
• Advanced Python, including testing, packaging and writing code that other engineers can maintain.
• Daily use of AI engineering tools such as Claude Code or Codex, including reviewing, testing and correcting the code they generate.
• Hands-on experience integrating LLMs into applications, including RAG, prompt design, tool use and evaluation.
• Experience building agents with the Model Context Protocol (MCP) or agent frameworks.
• Experience training and deploying machine learning models with libraries such as scikit-learn, PyTorch or XGBoost.
• Strong SQL and experience building data pipelines.
• Experience deploying solutions on at least one cloud platform, preferably Azure or AWS, using containers and CI/CD.
• The ability to explain technical trade‑offs to non-technical executives in plain language.

DESIRED (NOT REQUIRED):

• Experience with data platforms such as Databricks, Snowflake or Microsoft Fabric, and transformation tools such as dbt.
• Experience with LLM evaluation and observability tools, such as MLflow, Langfuse or LangSmith.
• Knowledge of LLM security risks, such as those in the OWASP Top 10 for LLM Applications.
• Experience running training sessions or workshops for technical or business audiences.