Lead Architect – AI Enablement & Automation (.NET)

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 11 hours ago

Vacancies: 1 Vacancy

Job Summary

We are seeking a highly experienced Lead Architect AI Enablement & Automation (.NET) to drive the AI transformation of our clients 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 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 .
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 runtime.

Qualifications :

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.

Additional Information :

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

At Endava were 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 perspectivesbecause 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.

Remote Work :

Employment Type :

Full-time

We are seeking a highly experienced Lead Architect AI Enablement & Automation (.NET) to drive the AI transformation of our clients engineering organization.This role combines enterprise-level architectural leadership with hands-on AI automation delivery.The architect will operate across two strateg...