.NET Micro-services Lead
  • Nityo Infotech Corp
1 Days Ago
NA
C2C, Yearly
Houston-TX
7-10 Years
Required Skills: C#, .NET,
Job Description
Role Overview:
We are seeking a hands-on .NET Microservices Lead to define, build, and govern large-scale enterprise platforms. The role requires deep expertise in C#, .NET, distributed systems, AWS /Azure, security, performance engineering, and production reliability. The architect must remain close to the code, lead critical implementations, guide multiple engineering teams, and translate business goals into secure, scalable, and operable solutions. Practical exposure to AI/LLM integration is preferred.
Required Technical Expertise:

Area

Required Expertise

C# and .NET

Expert C# 12 and .NET 8, ASP.NET Core, .NET, multithreading, concurrency, CLR internals, design patterns, clean architecture, enterprise integration patterns, and automated testing.

Microservices

Service decomposition, DDD, API design, event-driven architecture, resiliency, distributed data, service discovery, configuration, and backward-compatible evolution.

Messaging

Kafka, SQS, SNS, Event Bridge or RabbitMQ; delivery semantics, schema evolution, ordering, replay, dead-letter handling, and idempotent consumers.

Data

PostgreSQL/MySQL/Oracle plus DynamoDB or MongoDB; modeling, query tuning, indexing, partitioning, transactions, replication, caching, and data lifecycle.

Cloud and DevOps

AWS architecture, Docker, Kubernetes, CI/CD, Terraform or CloudFormation, observability, automated testing, deployment strategies, and SRE practices.

Security

Application, API and cloud security; OAuth2/OIDC/JWT, IAM, encryption, secrets, threat modeling, vulnerability remediation, and secure SDLC.

AI and Engineering Innovation:
  •  Evaluate and integrate AI/LLM capabilities using AWS Bedrock, OpenAI APIs, RAG patterns, embeddings, vector databases, and appropriate guardrails.
  •  Guide secure and responsible AI integration, including data protection, prompt and output controls, observability, evaluation, cost, latency, and fallback behavior.
  •  Use AI-assisted engineering for code understanding, test generation, documentation, troubleshooting, and productivity improvements without weakening engineering controls.

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