Zensar Technologies—AES - DE - Generative AI Application Developers

Job description

Description: We are looking for an experienced AI Orchestrator to drive AI-led transformation of the Software Development Lifecycle (SDLC) through agentic workflows, automation, and human-in-the-loop governance. The ideal candidate will design and implement AI agents that streamline requirements, architecture, development, testing, deployment, and support processes while ensuring appropriate oversight and quality controls. Requirements: Required Skills & Experience - 8+ years of software engineering experience with strong expertise in .NET/C#/ASP.NET Core applications. - Hands-on experience building AI agents, agentic workflows, and orchestration frameworks. - Experience with AI frameworks such as Semantic Kernel, Azure AI Foundry, Copilot Studio, LangGraph, LangChain, AutoGen, or CrewAI. - Strong understanding of SDLC, DevOps, CI/CD, testing, and enterprise architecture principles. - Experience implementing human-in-the-loop governance and AI guardrails. - Strong analytical and solution architecture skills with the ability to recommend enterprise-wide application and platform improvements. Preferred Qualifications - Experience with Azure cloud services, GitHub Copilot, Azure DevOps, and DevSecOps practices. - Knowledge of RAG, MCP, vector databases, and enterprise AI governance frameworks. - Experience leading AI-driven engineering transformation initiatives. Success Profile A technology leader who combines AI orchestration, software architecture, .NET expertise, and SDLC transformation experience to create scalable agent-driven delivery models while maintaining strong governance and human oversight. Responsibilities: Key Responsibilities - Design and orchestrate AI agents and multi-agent workflows across the SDLC. - Build loop-based workflows that enable planning, execution, validation, feedback, and continuous improvement. - Automate engineering activities including requirements analysis, code generation, testing, documentation, code reviews, and release validation. - Define human-in-the-loop checkpoints for governance, risk mitigation, and quality assurance. - Assess existing .NET applications and recommend architectural improvements, modernization opportunities, and AI integration patterns. - Partner with architects, developers, and business stakeholders to establish AI-first engineering practices. - Measure and optimize productivity, quality, and SDLC cycle-time improvements delivered through AI.