Overview
The Agentic AI Developer will design and implement production-grade agentic AI systems that integrate large language models (LLMs) with tools, data sources, and enterprise workflows.
Key Responsibilities
- Architect and implement multi-agent and single-agent AI systems using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent.
- Design agent reasoning loops, tool-calling mechanisms, memory management strategies, and orchestration logic.
- Develop agents capable of interacting with APIs, databases, document repositories, and internal WBG systems.
- Implement retrieval-augmented generation (RAG) pipelines to ground agents in institutional knowledge and internal data.
- Integrate leading LLM providers (Anthropic Claude, OpenAI GPT, Google Gemini) into agent workflows via APIs.
- Design and refine system prompts, few-shot examples, and chain-of-thought instructions to guide agent behavior reliably.
- Implement guardrails, output validation, and fallback logic to ensure safe and accurate agent responses.
- Build custom tools and function-calling interfaces that agents can invoke.
- Ensure tool schemas are well-defined, type-safe, and robust against edge cases.
- Integrate with internal WBG platforms and data sources as required.
- Design evaluation frameworks to assess agent performance, accuracy, groundedness, and safety.
- Conduct red-teaming and adversarial testing to identify failure modes and jailbreak risks.
- Establish benchmark datasets and automated testing pipelines for regression monitoring.
- Package and deploy agentic applications on cloud infrastructure (Azure, GCP, or equivalent).
- Implement observability and monitoring for agent behavior, latency, and token usage.
- Maintain CI/CD pipelines for continuous delivery of agent updates.
- Produce technical documentation covering architecture decisions, agent workflows, and API specifications.
- Provide knowledge transfer sessions and code walkthroughs for internal engineering teams.
- Contribute to internal guidelines and best practices for agentic AI development at WBG.
Required Experience
- Minimum of 5 years of relevant experience with a Master's degree; or a minimum of 7 years of relevant experience with a Bachelor's degree.
- Minimum 5–7 years of software development experience, with at least 2 years focused on AI/ML systems.
- Demonstrated hands-on experience building agentic or LLM-powered applications in production environments.
- Experience with at least one agentic framework (LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Strong understanding of LLM APIs, function/tool calling, RAG architectures, and vector databases (e.g., Pinecone, Weaviate, pgvector).
- Experience with cloud platforms (Azure preferred; GCP or AWS acceptable).
- Familiarity with containerization (Docker, Kubernetes) and CI/CD practices.
- Prior experience working in international organizations or development finance institutions (preferred).
- Familiarity with knowledge management systems, document intelligence, or enterprise search (preferred).
Qualifications
Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field; or a Bachelor's degree in a related field.
