AI Systems & Automation Engineer

This role is responsible for developing intelligent tools, automation workflows, and system enhancements that reduce manual workload, improve uptime and service reliability, and contribute directly to business performance through cost savings or revenue‐impacting initiatives.

The position plays a key role in identifying automation opportunities, building scalable solutions, and embedding compliance controls into data‐related processes. Working closely with MIS, Data Governance, Compliance, and business teams, the AI Engineer/Developer ensures that all solutions are secure, reliable, and aligned with ITG‐PH’s innovation and governance objectives.

• AI Automation & Systems Intelligence
• Design, build, and deploy AI‐powered automation solutions that streamline workflows and reduce manual workload across the organization.
• Develop intelligent systems that improve decision-making, operational visibility, and predictive capabilities.
• Implement machine learning models, NLP pipelines, and automation frameworks aligned with business priorities.
• Integrate AI solutions with existing enterprise systems, ensuring reliability, scalability, and security.
• Automation Engineering (AI and Non‐AI)
• Identify high‐impact automation opportunities through process analysis, stakeholder interviews, and data-driven assessments.
• Develop non‐AI automation solutions (e.g., scripting, RPA, workflow automation) to complement AI initiatives.
• Build reusable automation components, templates, and frameworks to accelerate future deployments.
• Data Governance & Compliance
• Develop AI-assisted tools and automation mechanisms to ensure compliance with data regulations, internal policies, and process standards.
• Collaborate with Data Governance, Compliance, and Security teams to embed controls into automation workflows.
• Monitor data quality, lineage, and access patterns; implement automated alerts and remediation workflows.
• Support audits by generating automated compliance reports and evidence logs.
• Systems Reliability & SLA Management
• Implement monitoring, alerting, and self-healing automation to maintain 90%+ uptime and SLA compliance for business systems.
• Conduct root-cause analysis and deploy preventive automation to reduce recurring incidents.
• Optimize system performance through intelligent load balancing, anomaly detection, and predictive maintenance models.
• Innovation & Business Impact
• Pilot at least two AI-driven initiatives that directly contribute to revenue generation or measurable cost savings.
• Evaluate emerging AI technologies and recommend adoption strategies aligned with companies innovation roadmap.
• Document solutions, create technical playbooks, and mentor team members on AI and automation best practices.

• Internal
• MIS Team — Collaborate on system integration, monitoring, and deployment. Data Governance — Coordinate on data lineage, privacy, retention, and audit requirements.
• CQCI and ICT — Align on access controls, encryption, and regulatory controls for production systems.
• Department Heads — Partner to identify automation opportunities, define acceptance criteria, and validate business impact.
• Technology Heads — Translate business needs into prioritized roadmaps, user stories, and release plans. Work with cloud engineers and SREs for provisioning, CI/CD, observability, and incident response.
• Support and Service Desk (SOC) — Coordinate on ticket routing, alerting thresholds, and runbook handoffs for production automations
• External
• Vendors and Third‐party Providers — Manage relationships with cloud providers, ML platform vendors, RPA vendors, and data providers. Customers and Account Owners — Engage for pilot validation, feedback on automated insights, and acceptance testing.
• Regulators and Auditors — Provide evidence, reports, and controls for compliance reviews and audits.
• Consultants and Implementation Partners — Coordinate for specialized integrations, large pilots, or capability accelerations.

• Technical Skills
• Strong proficiency in Python, JavaScript, or similar languages used for AI and automation.
• Experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).
• Familiarity with LLMs, prompt engineering, and generative AI tools.
• Hands-on experience with automation platforms (RPA, workflow engines, scripting frameworks).
• Knowledge of cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Understanding of data governance, data privacy regulations, and compliance frameworks.
• Experience integrating APIs, microservices, and enterprise systems.
• Professional Competencies
• Strong analytical, problem‐solving and project management skills.
• Ability to translate business pain points into technical solutions.
• Excellent documentation and communication skills.
• Ability to work cross-functionally with business, compliance, and technical teams.
• Innovation mindset with a bias toward rapid prototyping and iterative improvement.

• Minimum: Bachelor’s degree in Computer Science, Software Engineering, Data Science, Statistics, Mathematics, or a closely related field.
• Preferred: Master’s degree in Machine Learning, Artificial Intelligence, Data Science, or Business Analytics.
• Relevant Certifications (preferred):
• Cloud: AWS Certified Machine Learning, Google Professional ML Engineer, Azure AI Engineer.
• Data/ML Ops: MLOps Foundation, Databricks Certification.
• Security and Governance: Certified Information Privacy Professional (CIPP) or equivalent.
• Automation: UiPath/Automation Anywhere/RPA certifications (if applicable)

• Years of Experience: 3–7 years
• Domain Experience: Proven delivery in enterprise environments such as FinTech, SaaS, BPO, retail, or logistics preferred.
• Technical Experience:
• End‐to‐end ML lifecycle: prototyping, validation, deployment, monitoring, and retraining.
• Automation engineering: RPA, workflow engines, scripting, and integration with enterprise systems (NetSuite, ERPs).
• Data engineering: building/consuming ETL pipelines, data validation, lineage, and quality frameworks.
• Real‐time systems: streaming data processing and real‐time anomaly detection.
• MLOps and observability: CI/CD for models, model monitoring, drift detection, and alerting.
• Cloud and infra: hands‐on with AWS/Azure/GCP, containers, and orchestration (Docker, Kubernetes).
• Proficiency in Java, Python, and Deluge scripting is required.
• Business Impact Experience: Delivered measurable outcomes such as workload reduction, uptime improvements, revenue uplift, cost savings, or forecast accuracy gains.
• Governance and Compliance Experience: Implemented PII handling, audit trails, and automated compliance checks in production systems.
• Soft Skills and Leadership: Cross‐functional collaboration, stakeholder management, clear technical documentation, and ability to translate technical tradeoffs to business stakeholders