6 days ago
Manila, PhilippinesMid Level / Senior
Responsibilities
- Lead the design, architecture, deployment, and quality standards for AI agents serving specific business domains.
- Develop prompts, system designs, AI skills, plugins, and best practices for prompt engineering.
- Architect integrations between AI systems and internal platforms, including MCP servers and enterprise data integration patterns.
- Analyze AI model outputs and iteratively improve prompt and system design.
- Design testing strategies and quality standards for AI-generated code and documentation.
- Establish and track KPIs and ROI metrics for AI systems and report business impact to stakeholders.
- Collaborate with software teams to identify automation opportunities and design scalable agentic workflows.
- Lead proofs of concept and prototypes before enterprise deployment.
- Mentor junior engineers and drive adoption of AI best practices across development teams.
- Document AI workflows, architectural decisions, and enterprise best practices.
- Lead discovery, requirements gathering, feasibility assessment, and ROI validation for AI-driven solutions.
Requirements
- 3–7 years of professional experience in software development and/or AI/ML engineering.
- Bachelor's degree in Computer Science, Information Technology, Mathematics, or a related discipline, or equivalent demonstrated expertise.
- Demonstrated experience leading technical initiatives or mentoring engineers.
- Strong software development foundation in Python, JavaScript, and/or .NET, with knowledge of code quality, testing, and architectural patterns.
- Strong communication skills for explaining AI concepts to technical and non-technical audiences.
- Preferred: 2+ years of hands-on experience with LLMs and prompt engineering.
- Preferred: experience designing and deploying agentic systems, complex workflow automation, API integrations, REST, GraphQL, software architecture, CI/CD pipelines, GitHub Actions, automated testing, and code review.
- Preferred: knowledge of prompt injection risks, AI security, responsible AI practices, Qdrant, Databricks, and enterprise data engineering or analytics platforms.
- Preferred: production experience in financial services, banking, or other regulated industries.
Tech Stack
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