Zealant Consulting Group

AI-Native Engineer 
(Candidates whose AI exposure is limited to using ChatGPT or Copilot without hands-on implementation experience of RAG Piepline and LLM Model will not be a fit for this role.)
Location: Noida, Uttar Pradesh Work Mode: Work From Office – 5 Days a Week Experience:1 to 2 Years Education: Candidates from Premium Engineering Colleges Only (IIT and NIT ONLY STRICTLY)
About the Role
We are looking for exceptional Junior AI-Native Engineers who are passionate about building, experimenting, and exploring the possibilities of Artificial Intelligence.
This opportunity is designed for fresh graduates and engineers with 1–2 years of experience from premium engineering colleges who have gone beyond conventional software development and actively build applications using AI.
We are looking for hands-on builders with practical experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI workflows. Simply using ChatGPT or GitHub Copilot is not enough. You should have a genuine interest in developing AI-powered solutions, experimenting with new approaches, and translating ideas into working prototypes.
If you enjoy writing code, solving challenging problems, testing ideas, and learning rapidly, this role offers an opportunity to work at the forefront of applied AI engineering.
Key Responsibilities
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AI Prototyping: Build and iterate on AI-powered applications, proof of concepts, and experimental solutions.
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LLM Application Development: Implement LLM-based solutions using APIs or open-source models.
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RAG Development: Develop retrieval-augmented pipelines involving document processing, embeddings, retrieval, and context management.
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Agentic AI Workflows: Experiment with AI agents, tool calling, execution loops, multi-step workflows, and task automation.
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AI Evaluation: Test AI-generated outputs, evaluate solution quality, identify failure cases, and improve system reliability.
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Structured Engineering: Follow disciplined development workflows such as Test-Driven Development (TDD), gstack, or similar approaches.
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Rapid Experimentation: Explore new AI frameworks, models, tools, and techniques to identify practical applications.
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Continuous Improvement: Debug implementations, refine approaches, and turn experimental ideas into functional solutions.
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Required Skills &amp; Qualifications
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Graduate from a premium engineering college with a degree in Computer Science, Engineering, or a related technical discipline.
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Freshers and candidates with 1–2 years of relevant experience are welcome.
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Strong programming fundamentals in Python or another relevant programming language.
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Hands-on experience building applications using LLMs and LLM APIs.
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Practical implementation experience with RAG, embeddings, semantic search, or vector databases.
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Understanding of agentic AI concepts, including tool calling, multi-step workflows, and execution loops.
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Familiarity with structured development practices such as TDD, automated testing, Git, and version control.
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Strong analytical thinking, problem-solving ability, and a willingness to experiment.
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Evidence of practical AI development through personal projects, GitHub repositories, hackathons, internships, or working prototypes.
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Good to Have
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Exposure to LangChain, LangGraph, LlamaIndex, or similar AI frameworks.
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Familiarity with vector databases, embedding models, and AI evaluation techniques.
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Experience building AI agents or automating multi-step tasks using LLMs.
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Exposure to gstack or similar AI-assisted development workflows.
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Contributions to open-source projects or participation in AI/engineering hackathons.
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What We’re Looking For
We value engineers who demonstrate a genuine builder mentality.
You should be someone who:
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Builds AI applications instead of relying exclusively on chat-based tools.
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Can explain the architecture, implementation, and limitations of projects you have developed.
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Enjoys experimenting with LLMs, RAG pipelines, and agentic systems.
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Approaches development systematically through testing, debugging, and iterative improvement.
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Learns quickly and is comfortable working on ambiguous, evolving problems.
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Eligibility Criteria: Premium engineering college graduates only, with 1–2 years of experience and demonstrable hands-on AI development skills.