AI Engineer

Position: AI Engineer

Duration: Permanent

Location: Mandaluyong City, Metro Manila, Philippines

Work Mode: Fully Onsite

Work Shift: Day Shift (9:00 AM or 10:00 AM)

Job Qualifications:

• Bachelor's degree in Computer Science, Mathematics, Applied Physics, Data Science, or a related field

• 5 to 8 years in data engineering, platform engineering, MLOps, or operations-focused technical role.

• Hands-on experience building automation solutions or data pipelines in a production environment.

• Exposure to AI/ML tooling, agentic frameworks, or LLM-based applications is a strong plus.

• Comfortable working in ambiguous environments, able to identify problems and propose solutions independently.

Operational Outcomes

•Systems thinking can zoom out to see the operational picture, then zoom in to implement a targeted solution.

• Automation mindset: instinctively asks & can this be automated & before accepting manual work as the norm.

• Data fluency: comfortable with SQL, querying operational data, and turning metrics into insights.

• Clear communication: able to explain technical solutions to non-technical stakeholders and document work for future maintainability.

• Collaborative: works well across engineering, operations, and leadership without needing constant direction.

• Understands agentic AI concepts and design patterns, including tool use, memory, multi-agent orchestration, and human-in-the-loop workflows, and can translate these into practical operational use cases.

Plus Points:

• Proficient in Python, Pandas, Polars, and Apache Spark.

• Hands-on experience with LangChain, LangGraph, RAG pipelines, and prompt engineering.

• Familiar with PostgreSQL and Apache Iceberg

• Understand end-to-end AI architecture - RAG, agentic systems, multi-model workflows, APIs, and production deployment.

• Knowledgeable in ethical AI, bias mitigation, transparency, AI governance, and enterprise regulatory considerations.

• Strong grasp of data modelling, feature stores, dimensional modelling, and dataset structure for ML pipelines.

Job Responsibilities:

Support Enablement & Readiness:

• Analyse service onboarding processes to uncover bottlenecks, knowledge gaps, and manual steps that slow time-to-support.

• Deploy agentic AI capabilities to assist with documentation discovery, runbook navigation, and contextual knowledge synthesis.

• Standardize onboarding assets and readiness criteria, reducing reliance on tribal knowledge and SME dependency.

Operational Toil Reduction:

• Identify recurring operational pain points and evaluate them as candidates for automation.

• Assess the feasibility and impact of proposed automation ideas and maintain a prioritized backlog of improvement initiatives.

• Drive execution and track outcomes against measurable targets.

Agentic Operations:

• Validate agentic use cases across detection, triage, investigation, remediation, and documentation workflows.

• Map current operational workflows to surface automation gaps and build a roadmap for a scalable agentic operations ecosystem.

• Proactively generate new use cases that complement existing initiatives and maximize operational impact.

Visibility, Insights & Dashboards:

• Design and maintain operational dashboards that surface real-time and trend-based insights across workflows and service health.

• Translate raw operational data into clear, decision-ready visualizations for engineers and leadership.

• Identify blind spots in current observability and collaborate with stakeholders to continuously improve what gets measured.

AI Onboarding & Future Initiatives:

• Drive AI tool adoption across the team through enablement, documentation, and hands-on guidance.

• Explore emerging AI capabilities and assess their fit for unmet operational needs.