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Lead Architect – AI Enablement & Automation (.NET)

EndavaBengaluru
On-site Full-time

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Experience Level

Mid to Senior

Qualifications

Required Skills & ExperienceExperience: 13-16 yearsMust-Have Experience:. NET Ecosystem: Expertise in C#, . NET 8/Core, Microservices architecture, and reusable NuGet packages/frameworks. AI Orchestration: Practical experience with Semantic Kernel, AutoGen, or LangChain (. NET preferred). Automation & Agents: Proven track record of deploying Function Calling (Tools), multi-agent systems, and autonomous workflows. Data & Search: Expertise in Vector Databases (Azure AI Search, Pinecone, Qdrant) and hybrid search methodologies. DevOps / MLOps: Familiarity with GitHub Actions, Azure DevOps, CI/CD pipelines, and AI observability (latency, cost, accuracy metrics). Cloud Platforms: Strong understanding of cloud technologies, particularly Azure.

About the job

We are looking for a seasoned Lead Architect – AI Enablement & Automation (. NET) to spearhead the AI transformation within our client’s engineering organization.

This role merges enterprise-level architectural leadership with hands-on AI automation execution.

The architect will focus on two strategic areas:

  1. Enablement – Build scalable AI frameworks that empower . NET engineering and QA teams.
  2. Automation – Create and implement production-ready AI-driven workflows that address significant business challenges.

Key Responsibilities

1. Enablement Pillar – Promoting AI Integration Across Engineering

Enterprise AI Architecture

  • Define and establish architectural guidelines for AI integration within . NET 8/Core microservices.
  • Set standards for secure, scalable, and cost-effective AI utilization.

Shared AI Infrastructure

  • Design and implement a Common AI Service Layer utilizing frameworks like Semantic Kernel or LangChain. NET.
  • Develop centralized features including:
    • Authentication & secure API access
    • Rate limiting & throttling
    • Cost tracking & observability
    • Model routing & fallback strategies

Developer Acceleration

  • Create reusable NuGet packages, SDKs, and frameworks to streamline AI integration.
  • Develop project templates and CI/CD pipelines to facilitate the deployment of AI-enabled components.
  • Integrate AI best practices into engineering workflows.

Upskilling & Mentorship

  • Lead a Community of Practice (CoP) focused on AI integration.
  • Mentor C# engineers in:
    • Vector search techniques
    • Prompt engineering principles
    • RAG patterns
    • LLM orchestration & tool usage
  • Establish technical governance and AI engineering standards.

2. Automation Pillar – Proven AI Delivery at Scale

Agentic Workflow Design

  • Architect and implement multi-agent systems that can:
    • Execute intricate business logic
    • Interface with legacy systems and databases
    • Perform autonomous task orchestration

Production-Grade RAG Implementation

  • Develop sophisticated Retrieval-Augmented Generation (RAG) systems utilizing:
    • Hybrid Search (Vector + Keyword)
    • Semantic re-ranking
    • Data chunking & partitioning techniques
  • Ensure high-accuracy AI-driven support and automation systems.

About Endava

Endava is a leading technology company that specializes in delivering innovative solutions, leveraging a unique blend of agile and continuous delivery practices, with a deep focus on client satisfaction and operational excellence.

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