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AI/ML Engineer | Hybrid - QC/Makati at Tasq Staffing Solutions, Inc.
Quezon City, Metro Manila, Philippines -
Full Time

Start Date

Immediate

Expiry Date

15 Oct, 26

Salary

0.0

Posted On

17 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

ML Engineering, GenAI, LLM, RAG, Prompt Engineering, Azure AI, Microsoft Fabric, Databricks, Python, SQL, MLOps, CI/CD, Azure DevOps, Git, Lakehouse Architecture, Responsible AI

Industry

Staffing and Recruiting

Description
Role Overview: Work set-up: Hybrid 3x / RTO 2x per week | Eton, Centris Work shift: Nightshift This role focuses on building and deploying AI/ML solutions within enterprise data ecosystems. Key responsibilities include developing ML models and GenAI/LLM solutions (RAG, prompt engineering), integrating AI into data pipelines and platforms like Fabric and Databricks, ensuring production readiness (monitoring, performance), translating AI outputs into business insights, and upholding Responsible AI governance—all within an Agile delivery framework. Technical skills span: ML Engineering: model lifecycle, feature engineering, training/deployment Cloud AI & Data Platforms: Azure AI/OpenAI, Microsoft Fabric, Databricks (MLflow, Delta Lake), Lakehouse architecture Data Engineering: Python/SQL, pipelines, orchestration MLOps: CI/CD, versioning, monitoring, observability Tooling: Azure DevOps, Git Soft Skills include analytical problem-solving, stakeholder communication, cross-team collaboration, documentation discipline, and adaptability in fast-paced Agile/AI environments. Domain Knowledge covers enterprise data platforms, ServiceNow, data governance/compliance, and Responsible AI principles (fairness, transparency, bias mitigation, auditability). Other Non-Negotiable Requirements: At least 2 years of equivalent work experience Finished a bachelor's degree in any specialization
Responsibilities
Develop and deploy AI/ML solutions, including GenAI and LLM models, within enterprise data ecosystems. Integrate these solutions into data pipelines and ensure production readiness through monitoring and Responsible AI governance.