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Lead Engineer QA-(Automation, API, Performance, Security)

NeST Digital -
Kochi, Kerala

Apply Now

Job details

Full-time

Qualifications

CI/CD
Performance tuning
ASP.NET
Elasticsearch
Azure
MVC
React
Kubernetes
Software deployment
Enterprise Software
DevOps
.NET Core
Microsoft SQL Server
Databases
Microservices
Database design
Docker
Machine learning
Continuous integration
Customer relationship management
Mentoring
Angular
Scrum
APIs
Agile
Web services
Kafka
AI
Communication skills
Banking
Entity Framework
Software architecture
SDLC
Design patterns

Full job description

Minimum Required Experience : 8 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

Lead Software Engineer

Experience

Minimum 8 years of experience in designing, developing, and implementing enterprise software applications, modules, interfaces, and components.
Proven experience leading technical teams and delivering complex software projects in an Agile environment.

Mandatory Technical Skills

Strong hands-on experience in .NET Core, ASP.NET MVC, and Entity Framework.
Experience with Angular /React or later versions
Strong expertise in AI-assisted software engineering, including the effective use of Generative AI tools for solution design, code generation, code optimization, code reviews, debugging, documentation, and developer productivity.
Strong proficiency in Microsoft SQL Server, including database design, performance tuning, and optimization.
Experience in writing and maintaining unit tests using NUnit.
Strong experience in designing and developing Microservices-based architectures.
Hands-on experience with Angular design patterns and frameworks, including NgRx, RxJS, and Angular Material.
Strong understanding and implementation experience of the CQRS (Command Query Responsibility Segregation) design pattern.
Experience with Apache Kafka for event-driven and asynchronous messaging. - Any queue services
Experience using the ELK Stack (Elasticsearch, Logstash, and Kibana) for centralized logging, monitoring, and troubleshooting.

Technical Leadership

Collaborate closely with Solution Architects to define scalable, secure, and maintainable solution architectures.
Lead technical design discussions, architecture reviews, and code reviews to ensure adherence to engineering best practices.
Provide technical guidance and mentorship to development teams, promoting coding standards, quality, and continuous improvement.
Drive the adoption of modern engineering practices, automation, and AI-assisted development across the team.
Ensure technical solutions align with business objectives, performance requirements, and scalability goals.

Project & Delivery Management

Strong understanding of software architecture, design principles, and the complete Software Development Life Cycle (SDLC), from requirements through deployment and production support.
Identify technical risks and implementation challenges early, and proactively drive mitigation strategies with the team.
Support solution estimation, sprint planning, task prioritization, and resource allocation to ensure successful project delivery.
Work closely with cross-functional teams to ensure timely and high-quality product releases.
Possess a good understanding of change management, release planning, and risk mitigation.

Stakeholder & Client Management

Proven experience managing client interactions and leading technical discussions with customers and stakeholders.
Collaborate effectively with architects, product owners, business analysts, QA teams, and other stakeholders to finalize solution designs and interface specifications.
Demonstrate strong stakeholder management skills, effectively managing expectations and ensuring transparency throughout project execution.
Excellent written and verbal communication skills with the ability to present technical concepts to both technical and non-technical audiences.

Preferred Skills

Experience with CI/CD pipelines and Azure DevOps.
Knowledge of containerization technologies such as Docker and Kubernetes.
Experience with cloud platforms, preferably Microsoft Azure.
Familiarity with secure coding practices and performance optimization techniques.

Agile & Delivery Experience

Extensive experience working in Agile/Scrum development environments, actively participating in sprint planning, backlog refinement, daily stand-ups, sprint reviews, and retrospectives.
Experience leading Agile development teams and collaborating with Product Owners, Scrum Masters, Architects, and Business Analysts to ensure successful sprint execution and timely delivery.
Strong understanding of Agile engineering practices, including continuous integration, continuous delivery (CI/CD), iterative development, and DevOps collaboration.
Ability to estimate user stories, plan sprint capacity, prioritize technical tasks, and drive predictable delivery while maintaining high quality.
Experience in mentoring team members on Agile best practices and fostering a culture of continuous improvement and engineering excellence.

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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