AI Engineer
D2B Open · verified Oct 24, 2026 At a glance
Mid-level AI Engineering role at D2B. Remote, Philippines · full-time.
Pay not stated AI Engineer salary
remoteroles summary, based on the employer's posting.
What you'll do
- Shape each client solution around its problem and budget, selecting models, retrieval, agents or custom approaches
- Connect language models to client systems, designing prompts, tool use and agent workflows
- Assess client data, build model pipelines, and train and deploy forecasting or anomaly-detection models
- Track production accuracy, drift, speed and cost, then improve solutions and hand them over with training and documentation
What you bring
- At least five years in professional software engineering, including two years building AI applications used in production
- You have delivered fixed-scope work to external clients in consulting or professional services
- You bring advanced Python skills, hands-on LLM integration experience, and daily use of AI coding tools with code review and testing
- You have built agents, trained and deployed machine learning models, and worked with SQL, data pipelines and cloud deployments
Who this fits
This role suits a mid-level engineer comfortable working directly with a founder and client executives, business users and IT teams from scoping through handover. You’ll need to explain technical choices in plain language and build solutions client teams can maintain. The role is remote in the Philippines and follows Australian working hours and holidays.
From the employer
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
Requirements
- 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.
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