Sr. GenAI Engineer

• The platform should enable teams to build, deploy, and operate AI agents using a variety of development approaches and frameworks while providing a standardized enterprise foundation for orchestration, integration, governance, security, and observability.

• The engineer should have experience designing agentic workflows and multi-agent orchestration, including both event-driven and workflow-based patterns. The platform should support scalable communication and coordination between agents, enterprise systems, tools, APIs, and diverse data sources.

• A key responsibility will be establishing enterprise-grade observability across the platform, including centralized instrumentation, tracing, operational metrics, performance monitoring, error tracking, and visibility into agent execution and behaviour.

• The platform should also provide a centralized gateway for AI model interactions, agent tools, and external services, ensuring consistent security, governance, authentication and authorization, access control, throttling, monitoring, and policy enforcement.

• The ideal engineer will have experience building scalable, extensible, and technology-agnostic AI platforms that allow different teams and business domains to develop agents independently while adhering to common enterprise standards for interoperability, security, governance, reliability, and operational management.

• The role should also focus on building production-grade AI engineering capabilities, including Agent Harness Engineering, automated and closed-loop evaluation, feedback loops, prompt and model evaluation, observability, guardrails, resiliency, and continuous improvement mechanisms. The goal is to create a reusable platform where multiple business domains can build, deploy, monitor, and operate autonomous agents using standardized enterprise patterns rather than creating isolated agent solutions.

RAG Pipeline, LLM, Enterprise Applications, AgenticAI, Python, Azure