Role Overview

We are seeking a self-driven Senior AI-Enabled .NET Engineer / Solution Architect to design and deliver intelligent, secure, scalable, and well-architected enterprise solutions.

AI is a primary focus of this role. The successful candidate will combine deep .NET and architecture expertise with practical knowledge of generative AI, AI agents, enterprise data, API ecosystems, Azure AI Foundry, and Google Cloud AI. The role requires the ability to identify valuable AI use cases and integrate them into production-grade web, mobile, cloud, hybrid, and on-premises environments.

Key Responsibilities

· Design, develop, and operate AI-enabled enterprise applications using C#, .NET, and ASP.NET Core.

· Identify opportunities to apply AI to business-process automation, decision support, customer experiences, and internal operations.

· Translate business requirements into AI use cases, process flows, domain models, data models, integrations, and maintainable code logic.

· Build generative AI solutions using large language models, RAG, embeddings, vector search, tool calling, and agentic workflows.

· Design and implement AI solutions using Azure AI Foundry and Google Cloud AI services.

· Integrate AI models securely with applications, APIs, databases, documents, and enterprise platforms.

· Design, publish, secure, govern, monitor, and manage APIs using Azure API Management and Google Cloud Apigee.

· Establish API standards covering lifecycle management, versioning, authentication, authorization, throttling, quotas, caching, transformation, routing, analytics, and developer onboarding.

· Design API gateways for internal, partner, public, mobile, microservices, and AI or model endpoints.

· Implement controls for AI APIs, including model routing, token quotas, prompt and response filtering, data-loss prevention, cost controls, observability, and responsible-AI guardrails.

· Design solutions across Azure, GCP, on-premises, hybrid-cloud, and multi-cloud environments.

· Build APIs, microservices, event-driven systems, integrations, and data pipelines supporting AI and conventional workloads.

· Work hands-on with SQL Server, PostgreSQL, Azure Cosmos DB, and vector-search technologies.

· Deploy and operate containerized applications using Docker and Kubernetes.

· Collaborate across business, product, AI, data, frontend, mobile, platform, security, infrastructure, and operations teams.

· Take end-to-end ownership from discovery and architecture through production deployment and continuous improvement.

AI and Machine-Learning Capabilities The ideal candidate should have strong knowledge of:

· Generative AI,



large language models, multimodal models, and enterprise AI application patterns.

· Azure AI Foundry and Google Cloud AI platforms.

· RAG architecture, document ingestion, chunking, embeddings, vector databases, metadata filtering, and semantic or hybrid search.

· AI agents, workflow orchestration, tool and function calling, grounding, memory, and human-in-the-loop patterns.

· Prompt engineering, structured outputs, context management, and reusable prompt templates.

· Model selection and evaluation based on quality, accuracy, hallucination risk, latency, scalability, token usage, and cost.

· AI testing, observability, tracing, feedback loops, evaluation datasets, and production monitoring.

· Responsible AI, privacy, content safety, prompt-injection defense, access control, auditability, and secure model integration.

· AI-assisted engineering tools and validation of their outputs for correctness, security, maintainability, and licensing risks.

API Management and Integration Expertise

· Strong hands-on experience with Azure API Management and Google Cloud Apigee.

· API-first architecture and governance across Azure, GCP, hybrid, and on-premises environments.

· REST, asynchronous APIs, webhooks, and familiarity with GraphQL and gRPC.

· OpenAPI specifications, API documentation, developer portals, API catalogs, and reusable API products.

· OAuth 2.0, OpenID Connect, JWT, managed identities, API keys, mutual TLS, certificates, and service-to-service security.

· API policies covering validation, transformation, routing, caching, rate limiting, quotas, retries, circuit breakers, and threat protection.

· API versioning, backward compatibility, deprecation, monetization, and lifecycle management.

· API analytics, distributed tracing, logging, monitoring, service-level objectives, and operational troubleshooting.

· Integration of API gateways with Kubernetes, ingress controllers, service meshes, serverless services, and on-premises systems.

· Exposure and protection of AI models, agents, tools, and RAG services through governed API layers.

Core Engineering and Architecture Skills

· Strong experience with C#, .NET, ASP.NET Core, REST APIs, and distributed systems.





· Sound knowledge of solution architecture, microservices, modular architecture, domain-driven design, event-driven systems, and enterprise integration patterns.

· Strong understanding of well-architected principles covering security, scalability, resilience, availability, observability, performance, and cost.

· Practical experience with Azure, GCP, on-premises infrastructure, and hybrid or multi-cloud architecture.

· Strong database and data-engineering capabilities, including:

· SQL Server and PostgreSQL

· Azure Cosmos DB and NoSQL modeling

· Data modeling and schema design

· Query optimization, indexing, transactions, and performance tuning

· Data flows, lineage, quality, governance, and integration

· Experience with Docker, Kubernetes, automated testing, CI/CD, monitoring, and infrastructure as code.

· Working knowledge of React.js and Next.js.

· Familiarity with Golang and the ability to understand or contribute to Go-based services.

· Understanding of native mobile application architecture, integration, security, offline behavior, notifications, and release processes.

Preferred Experience

· Delivery of production AI assistants, copilots, intelligent search, automation, recommendation solutions, or AI agents.

· Azure AI Search, PostgreSQL with pgvector, or equivalent vector platforms.

· AI gateways, model routing, semantic caching, fallback strategies, and AI cost controls.

· Messaging and streaming technologies such as Azure Service Bus, Event Hubs, Google Pub/Sub, or Kafka.

· Infrastructure as code using Terraform, Bicep, or equivalent tools.

· React Native, Flutter, Swift, or Kotlin.

· Modernization of legacy systems into API-led, AI-enabled, cloud-native, or hybrid solutions.

· Relevant Microsoft Azure, Google Cloud, Apigee, Kubernetes, .NET, or architecture certifications.

Skilled Competencies

· Maintains an AI-first mindset while recognizing when conventional software provides a better solution.

· Self-driven and able to own delivery from business discovery through production operation.

· Does not work in silos; actively collaborates, communicates, documents decisions, and shares knowledge.

· Connects business processes, application logic, enterprise data, APIs, and AI capabilities into practical solutions.

· Proactively communicates progress, assumptions, risks, dependencies, and architectural trade-offs.

· Remains hands-on while contributing to technical strategy and architectural governance.

· Balances innovation with business value, security, reliability, maintainability, delivery speed, and cost.

📌 Senior AI-Enabled .NET Engineer / Solution Architect (India)
🏢 Unicorn Lab
📍 India