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Open nowFirst seen 4 days ago

Senior AI Engineer (Agentic AI)

PM Consulting1,477 open roles

Where
Taguig, Metro Manila, Philippines
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Your applicationOpen nowSenior AI Engineer (Agentic AI)PM Consulting · Taguig, Metro Manila, Philippines

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. PM Consulting postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: first seen 4 days ago

PM Consulting median: 1 days open

The posting

Our client, a leading enterprise enterprise, is seeking a Senior AI Engineer specializing in Agentic Workflows and LLM Integration. This specialized engineering role sits at the cutting edge of AI innovation, commanding an organization-wide mandate to design, deploy, and own multi-step autonomous agent systems. The successful candidate will build robust backend infrastructures, orchestrate tool-calling logic, and manage advanced retrieval architectures to deliver resilient, production-grade AI applications within an enterprise framework.

Key Accountabilities

AI Architecture & Agent Workflow Engineering

  • Agent Execution Patterns: Design, build, and test highly complex, multi-step agent workflows utilizing established advanced architectural design patterns such as ReAct, planner-executor, and complex tool-chaining.
  • LLM Core Integration: Integrate flagship Large Language Models (including Anthropic Claude and OpenAI) with legacy enterprise APIs and internal microservices, engineering robust fault tolerance for retries, edge cases, and degraded ecosystem states.
  • Orchestration & Failure Resilience: Implement programmatic tool calling, function orchestration pipelines, and automated compensating actions to guarantee agent workflows remain stable under catastrophic or unexpected third-party failure conditions.
  • Human-in-the-Loop Controls: Architect and deploy conditional human-in-the-loop validation frameworks, including automated executive approvals, smart escalations, and exception-handling logic mandated by business governance or risk considerations.

Data Engineering, Prompts & Retrieval (RAG)

  • Context & Memory Architecture: Build and manage advanced agent memory retention layers and data retrieval mechanisms utilizing vector databases and Retrieval-Augmented Generation (RAG), tuning indexing schemas to ensure relevant context.
  • Prompt Management: Develop, maintain, optimize, and version-control complex prompt logic, semantic routing rules, and supporting technical documentation in accordance with strict enterprise engineering standards.

Production Deployment, Security & Observability

  • Cloud Operations: Deploy mission-critical AI services into production cloud environments, actively monitoring logs, distributed traces, and telemetry metrics to rapidly isolate and patch behavioral anomalies.
  • Enterprise Governance: Ensure all deployed solutions strictly mirror enterprise-grade security controls, identity management requirements, and rigorous data governance protocols.
  • Reliability Engineering: Partner with QA and Core Operations teams to continually upgrade automated test coverage, build out operational runbooks, establish incident response protocols, and drive system performance optimizations.

Requirements

Education & Experience

  • Technical Tenure: Minimum of 4 years of hands-on experience developing backend or service-based software architectures using C# and/or Python.
  • AI Specialization: At least 1 year of production-level experience working directly with large language models, structured prompt engineering frameworks, or agentic AI-enabled systems.
  • Analytical Reasoning: Elite debugging skills with a proven capacity to reason across distributed APIs, asynchronous data flows, and non-deterministic AI system behaviors.

Technical Skills (Required)

  • Programming Ecosystems: Production-grade fluency in C# and/or Python, including async workflow patterns, service building, and automated test frameworks.
  • Cloud Platform: Microsoft Azure (encompassing compute, scalable storage, IAM identity models, and automated deployment pipelines).
  • LLM Integration & Tooling: Direct integration with Claude and/or OpenAI APIs (handling tool calling, prompt tokenization, rate limit mitigation, and state error handling).
  • Agent Orchestration Frameworks: Experience using Azure AI Foundry or Microsoft Agent Framework. Hands-on knowledge of LangGraph or LangChain is highly valued.
  • Vector Architectures: Experience with Pinecone or equivalent vector databases (handling structural indexing, query retrieval, and semantic relevance tuning).
  • APIs & Identity: RESTful API design, enterprise-grade service authentication, and secure service-to-service integrations.
  • Observability Suites: Distributed application logging, performance metrics tracking, and transaction tracing in production scales.

Preferred Qualifications (Desirable)

  • Containerization deployments utilizing Docker and Azure Container Services (AKS/ACA).
  • Experience architecting relational databases (SQL Server) and NoSQL document stores (Azure Cosmos DB).
  • Prior experience operating within highly regulated, compliance-driven, or audit-heavy corporate environments.
  • Foundational understanding of emerging AI Governance and AI Security (OWASP Top 10 for LLMs) paradigms.
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