Forward Deployed Engineer - Data / Reporting-Alabang

Role Summary

Design, build, and deploy enterprise-grade AI agents using code-first frameworks, with strong focus on agent architecture, tool integration, grounding/RAG, and secure identity patterns. Ensure agents are production-ready through measurable evaluation, traceable source attribution, human-in-the-loop controls, CI/CD deployment, and stakeholder-driven delivery.

Key Responsibilities

Agent Design & Build

• Design agent architectures - single-agent, multi-agent and orchestrator/worker patterns - selecting the right pattern for each use case.
• Build agents on Microsoft Copilot Studio and code-first frameworks (Semantic Kernel, LangGraph, LangChain, Autogen or equivalent).
• Engineer instructions, system prompts and reasoning flows; implement deterministic guardrails around non-deterministic model behavior.
• Define and implement tool/function calling - API actions, connectors, custom plugins - with robust error handling and fallback paths.
• Implement human-in-the-loop checkpoints, approval flows and escalation-to-human paths where decisions carry business or regulatory risk.
• Build agent memory and state management - conversation context, session state and long-term knowledge persistence.

Grounding & Knowledge Engineering

• Build RAG pipelines: chunking strategy, embedding selection, vector indexing, hybrid and semantic retrieval, re-ranking.
• Connect agents to enterprise knowledge sources - SharePoint, Dataverse, Graph connectors, relational stores and document repositories.
• Tune retrieval quality and diagnose grounding failures, hallucination and citation accuracy issues.
• Implement source attribution so every agent answer is traceable to its grounding evidence.

Integration & Deployment

• Integrate agents with enterprise systems - ServiceNow, Microsoft Graph, Power Platform, REST APIs and internal services.
• Implement authentication and authorization patterns - Entra ID app registration, SSO, managed identity, delegated vs application permissions, least privilege.
• Deploy agents through Teams, M365 Copilot, web channels and API endpoints as the use case requires.
• Build CI/CD pipelines for agent solutions with environment promotion (dev → test → prod) and version control.

Required Skills & Experience

• 5+ years of relevant professional experience.
• Demonstrable production delivery of at least two LLM-based or agentic applications - not prototypes or demos.
• Strong Python (preferred) and/or C#/.NET, with clean API design and solid software engineering fundamentals.
• Hands-on with an agent framework: Semantic Kernel, LangGraph/LangChain, Autogen, or equivalent.
• Deep prompt engineering skill: system prompt design, few-shot patterns, structured output, chain-of-thought control, instruction hierarchies.
• RAG implementation experience end to end, including vector stores (Azure AI Search, Chroma, pgvector or similar) and retrieval tuning.
• Azure AI Foundry experience - deployments, quotas, content filters, model routing.
• Tool/function calling design including schema definition, argument validation and failure recovery.
• Enterprise identity and API integration - Entra ID, OAuth 2.0, app registrations, scoped permissions.
• Evaluation discipline - able to prove an agent works with evidence, not anecdote.
• Excellent stakeholder communication; comfortable sitting with a business SME and turning a conversation into a working agent.

Preferred

• Experience in financial services, insurance or another regulated industry with formal AI governance gates.
• Model Context Protocol (MCP) server/client implementation experience.
• Multi-agent orchestration at production scale.
• Fine-tuning, distillation or small-language-model deployment experience.
• Power Platform breadth - Power Automate, Dataverse, custom connectors.
• Familiarity with agent cost management and chargeback/tokenomics modelling.