Lead Architect – AI Enablement & Automation (.NET)

We are seeking a highly experienced Lead Architect – AI Enablement & Automation (.NET) to drive the AI transformation of our client’s engineering organization.

This role combines enterprise-level architectural leadership with hands-on AI automation delivery.

The architect will operate across two strategic pillars:

• Enablement – Establish scalable AI foundations that empower .NET engineering and QA teams.
• Automation – Design and deploy production-grade AI-driven agentic workflows solving high-value business problems.

Key Responsibilities

1. Enablement Pillar – Scaling AI Adoption Across Engineering

Enterprise AI Architecture

• Define and implement architectural guardrails for AI integration within .NET 8/Core microservices.
• Establish standards for secure, scalable, and cost-efficient AI consumption.

Shared AI Infrastructure

• Design and develop a Common AI Service Layer using frameworks such as Semantic Kernel or LangChain.NET.
• Implement centralized capabilities including:• Authentication & secure API access
• Rate limiting & throttling
• Cost tracking & observability
• Model routing & fallback strategies

Developer Acceleration

• Build reusable NuGet packages, SDKs, and frameworks to standardize AI integration.
• Create project templates and CI/CD pipelines enabling teams to deploy AI-enabled modules as easily as standard Web APIs.
• Embed AI best practices into engineering workflows.

Upskilling & Mentorship

• Lead a Community of Practice (CoP) for AI adoption.
• Mentor C# engineers in:• Vector search concepts
• Prompt engineering
• RAG patterns
• LLM orchestration & tool usage

• Drive technical governance and AI engineering standards.

2. Automation Pillar – Proven AI Delivery at Scale

Agentic Workflow Design

• Architect and implement multi-agent systems capable of:• Executing complex business logic
• Interacting with legacy systems and databases
• Performing autonomous task orchestration

Production-Grade RAG Implementation

• Build advanced Retrieval-Augmented Generation (RAG) systems using:• Hybrid Search (Vector + Keyword)
• Semantic re-ranking
• Data chunking & partitioning strategies

• Deliver high-accuracy AI-driven support and automation systems.

AI Reliability & Operational Excellence

• Implement enterprise-grade reliability mechanisms:• Retry policies
• Fallback models (e.g., GPT-4 → Phi-3 or equivalent)
• Hallucination detection & validation frameworks

• Define observability standards for latency, cost, and accuracy.

Performance & Cost Optimization

• Optimize token consumption and inference latency.
• Implement semantic caching strategies.
• Tune memory and concurrency management within the .NET runtime.

 

Required Skills & Experience

Experience - 13-16 years experience 

Category

Must-Have Experience

.NET Ecosystem

Expert-level mastery of C#, .NET 8/Core, Microservices architecture, and building reusable NuGet packages/frameworks.

AI Orchestration

Hands-on production experience with Semantic Kernel, AutoGen, or LangChain (.NET preferred).

Automation & Agents

Proven experience deploying Function Calling (Tools), multi-agent systems, and autonomous workflows.

Data & Search

Expertise in Vector Databases (Azure AI Search, Pinecone, Qdrant) and hybrid search strategies.

DevOps / MLOps

Experience with GitHub Actions, Azure DevOps, CI/CD pipelines, AI observability (latency, cost, accuracy metrics).

Cloud Platforms

Strong experience with Azure (preferred) or AWS/GCP AI services.

Preferred Qualifications

• Experience leading AI transformation initiatives at scale.
• Strong knowledge of secure AI design patterns and governance.
• Experience integrating AI into legacy enterprise environments.
• Familiarity with LLM evaluation frameworks and benchmarking techniques.

 

Leadership & Soft Skills

• Strategic thinker with hands-on execution capability.
• Strong stakeholder communication and influencing skills.
• Ability to balance innovation with enterprise stability.
• Mentorship mindset with experience scaling engineering capability.

Success Metrics

• Reduction in AI adoption friction across engineering teams.
• Measurable improvements in AI reliability, cost efficiency, and latency.
• Successful deployment of enterprise-grade agentic automation solutions.
• Increased AI engineering maturity within the organization.

 

Candidate should be based in Bangalore and must be available to work from the client’s Electronic City office for 2 days a week

At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.