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.
