Modern Ballistic
Full Stack Development (UI Dev)
Skills
12 days ago
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Important information
Contract type:
Permanent
Salary:
₹2,000,000 – ₹3,500,000 / year
Location:
Gurugram, Haryana, India
Work mode:
Hybrid
Published on:
7 October 2026
What they need
Full Stack Development (UI Dev)
Modern developers in cloud SaaS environments must not only write code but also integrate AI assisted workflows, scalable data architectures, and secure development practices.
Key forward-looking skills:
- Experience using AI coding assistants (e.g. GitHub Copilot, Code Whisperer) to accelerate development while maintaining high code quality. • Familiarity with agentic software engineering workflows (particularly GitHub Copilot), including autonomous agents that plan and execute coding tasks. • Proficiency with .Net, C# and TSQL. • Understanding and implementation of Data Contracts for reliable data exchange across microservices or domains. • Experience designing and integrating with Data Mesh architectures, promoting decentralized ownership and discoverability of data assets. • Ability to build and monitor AI-powered features, such as personalized recommendations, predictive analytics, or generative content. • Familiarity with serverless, event-driven, and edge computing paradigms for building scalable modern SaaS platforms. • Hands-on experience with micro frontend architecture, enabling independent deployment, modular ownership, and better scalability across product teams ideally integrated with AI driven tooling to scaffold or refactor both front-end and back-end components. • Strong knowledge of secure coding practices, including the use of GitHub Advanced Security (e.g. CodeQL, secret scanning, dependency review) and other tools for real-time detection of code-level vulnerabilities during development. • Hands-on experience with leading agentic frameworks such as LangChain, Semantic Kernel, CrewAI, and similar platforms for building sophisticated AI agent systems and workflows. • Understanding of RDF (Resource Description Framework), SPARQL query language, and SHACL (Shapes Constraint Language) for data validation and modeling. Experience with semantic graph databases, particularly Stardog, and domain-specific ontologies including FIBO/CDM frameworks.
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