AI Native Backend Engineer (Core Java, 3 Months Contract
Staffnix
- Location
- Bengaluru, Karnataka, India
- Job type
- Full-time
Required skills
- backend
- end-to-end
- GitHub
- Java
- microservices
About the role
Website:
staffnix.com
Job details:
Read jd
Experience 3-6 year's only.
candidate must be from product company only.
Mandatory (Note1): This is a 3-month contract role. Candidate must be comfortable with the contract engagement and able to join immediately (urgent — onboarding within ~a week)
Strong Backend Software Engineer Profile with strong Core Java expertise
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Mandatory (Experience 1): Must have 2+ years of hands-on backend software engineering experience.
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Mandatory (Experience 2): Must be able to design and implement moderately complex features with minimal supervision, and contribute to system design and architecture discussions.
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Mandatory (Experience 3): Must be comfortable owning business-critical production components — maintaining connectors, handling customer escalations, security fixes, bug fixes, and building new features end-to-end.
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Mandatory (Tech skill 1): Must have strong, current Core Java depth with solid, clear coding fundamentals — Java is the primary skill for this role. Deep hands-on core engineering strength matters more than framework knowledge.
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Mandatory (Tech skill 2): Must have strong core backend engineering fundamentals and experience with scalable systems (connectors handle millions of events/data points daily across customers).
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Mandatory (Tech skill 3): Must have microservices experience.
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Mandatory (AI Fluency): Must have demonstrated, hands-on use of AI coding assistants (Claude, GitHub Copilot, Cursor, etc.) as a core part of the daily workflow — scaffolding, writing tests, reviewing code, and debugging — with the ability to write precise, effective prompts for production-quality output, decompose tasks so AI assistance is effective, and critically evaluate AI-generated code for correctness, security, and standards before merging.
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Mandatory (AI-Native): Must have actually built and deployed AI/LLM-powered features in production (e.g. RAG, LLM-based scoring, AI assistant) — demonstrable AI-native engineering, not merely using AI tools to write code.
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