Agentic AI Developer

Description

The role operates within established architectural direction and is accountable for delivering reliable, maintainable, and production ready solutions that meet business, security, and compliance requirements.

The role:

Design, build, and test multi‑step agent workflows using established patterns such as ReAct, planner‑executor, and tool‑chaining

Integrate LLMs (i.e. Claude and OpenAI) with enterprise APIs and internal systems, including robust handling of retries, edge cases, and degraded states

Implement tool calling, function orchestration, and compensating actions to ensure workflows remain stable under failure conditions

Design and implement human‑in‑the‑loop controls, including approvals, escalations, and exception handling where required by business logic or risk considerations

Collaborate with product managers, domain experts, and technical stakeholders to refine requirements and translate them into technical designs

Develop, maintain, and version prompt logic and supporting documentation in line with agreed standards

Build and manage agent memory and retrieval mechanisms using vector databases and retrieval‑augmented generation (RAG), ensuring context remains relevant and high‑signal

Deploy AI services to production environments and actively monitor logs, traces, and metrics to detect and resolve issues

Contribute to test coverage, operational runbooks, and incident response practices in collaboration with QA and operations teams

Support continuous improvement of system reliability, performance, and maintainability

Qualifications

Minimum 2 years’ experience developing backend or service-based systems using C# and/or Python

At least 1 year of hands‑on experience working with large language models, prompt engineering, or AI‑enabled systems

Strong analytical and debugging skills with the ability to reason across APIs, data flows, and AI system behavior

Ability to clearly communicate technical decisions and trade‑offs to technical and non‑technical stakeholders

Demonstrated ability to learn and adapt in a rapidly evolving technology environment

Programming: C#, Python (production services, async workflows, automated testing)

Cloud Platform: Azure (compute, storage, identity, deployment pipelines)

LLM Integration: Claude and/or OpenAI APIs (prompting, tool calling, rate limits, error handling)

Agent Orchestration: Azure AI Foundry, Microsoft Agent Framework; optional alternatives include LangGraph and LangChain.

Vector Databases: Pinecone or equivalent (indexing, retrieval, relevance tuning)

APIs & Integration: RESTful APIs, enterprise authentication, service‑to‑service integration

Observability: Application logging, metrics, and tracing in production environments