Lead Engineer-AI Projects (.NET Core & SQL Server)

NeST Digital -
Kochi, Kerala

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Job details

Full-time

Qualifications

CI/CD
ASP.NET
Elasticsearch
Azure
MVC
Software troubleshooting
React
Kubernetes
Software deployment
Enterprise Software
DevOps
.NET Core
Microsoft SQL Server
Git
English
Databases
Microservices
Database design
AWS
Analysis skills
Docker
Machine learning
Continuous integration
REST
Angular
Scrum
Software development
APIs
Agile
Web services
Kafka
AI
Communication skills
Banking
Entity Framework
Debugging

Full job description

Minimum Required Experience : 6 years

Full Time

Skills

Bank Transactions

Web Services

Banking Knowledge

Testing And Implementation Of Software/ Web Applications

Web Publishing

Build Core Web Application Design

Develop Front-End Web Pages

Artificial Intelligence

.Net Core web api

Web Development

Digital Banking

Dot Net Core Web Api

Azure Webapp

Description

Senior Software Developer – .NET Core

Experience

Minimum 5 years of experience in the design, development, and configuration of enterprise software modules, interfaces, and components.
Mandatory Technical Skills
Strong hands-on experience with .NET Core, ASP.NET MVC, and Entity Framework.
Strong proficiency in Microsoft SQL Server for database design, development, and optimization.
Experience in AI-assisted software development using Generative AI tools for code generation, code optimization, debugging, code review, and documentation.
Experience in writing unit tests using NUnit.
Strong understanding and implementation experience of Microservices Architecture.
Hands-on experience with the CQRS (Command Query Responsibility Segregation) design pattern.
Experience with Apache Kafka for event-driven and asynchronous messaging. - Any queue services
Experience with Git and Azure DevOps for source code management and CI/CD.
Strong understanding of RESTful APIs and service integration.
Preferred Skills
Experience with Angular /React or later versions.
Experience using the ELK Stack (Elasticsearch, Logstash, and Kibana) for centralized logging, monitoring, and troubleshooting.
Knowledge of containerization technologies such as Docker and orchestration using Kubernetes.
Familiarity with cloud platforms such as Microsoft Azure, AWS.
Responsibilities
Design, develop, and maintain scalable, secure, and high-performance software components.
Collaborate closely with Solution Architects to produce high-quality technical designs and implementation plans.
Develop and integrate RESTful APIs and microservices.
Participate in architecture discussions, design reviews, and code reviews.
Interact with business analysts, architects, QA teams, and other stakeholders to finalize technical designs and interface specifications.
Ensure adherence to coding standards, best practices, and quality guidelines.
Troubleshoot, optimize, and enhance application performance and reliability.
Soft Skills
Excellent verbal communication skills in English.
Strong analytical and problem-solving abilities.
Excellent interpersonal and stakeholder management skills.
Ability to work effectively in Agile/Scrum environments.
Self-motivated with a strong focus on quality and continuous improvement.

Must Have

Prompt Engineering – Ability to design effective prompts that optimize response quality, reduce token consumption, and accelerate development through efficient interactions with AI models.
Hands-on experience leveraging AI-assisted development tools – Proven experience using AI tools to accelerate end-to-end solution delivery, including requirements analysis, solution design, coding, testing, debugging, documentation, and deployment.

Good to Have

Knowledge of Agentic AI frameworks – Understanding of autonomous AI agents, multi-agent systems, orchestration patterns, tool integration, and workflow automation concepts.
Knowledge of Retrieval-Augmented Generation (RAG) patterns and AI-native development practices – Understanding of AI application architectures, RAG concepts, vector databases, and knowledge retrieval mechanisms to support the design and development of enterprise AI solutions.
Machine Learning (ML) knowledge and implementation experience – Familiarity with ML concepts, model development, training, deployment, and integration into business applications.

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Description

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